Performance Appraisal and Employee Performance Dissertation

Performance Appraisal and Employee Performance - Dissertation Example 53 List of Figures and Appendices Figure 1: Preference of companies for different performance appraisal measures†¦Ã¢â‚¬ ¦.. 32 Figure 2: Satisfaction levels of employee on different measures†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 32 Figure 3: Factors influencing motivation of employees†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦... 34 Figure 4: Alternative measures in influencing employee performance†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 35 Appendix 1: The Gantt Chart†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.... 46 Appendix 2: Questionnaire†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 47 I. Executive Summary Organizations in the modern times are all the more improving and adopting new measures towards their performances. Performance management has always been an integral part of every organization dealing with the employee performance study and determining ways to improve the employee performances. The current study deals with an understanding of the capability of a performance appraisal system in influencing the employee performance within an organization. The focus of the study has been to understand whether there is a need to incorporate alternative measures within organizations to effectively influence organizational employees. The study has taken both primary and secondary sources as its study materials. While the secondary sources based on the previous researches conducted on the concerned topic, the primary sources were based on the interviews and survey responses conducted on some of the companies in London, the participants being the organizational members and managers. The complete study has clearly reflected the fact that although performa nce appraisal systems are doing well and are quite significant in... From this dissertation it is clear that employees are an organization’s most important assets. Their satisfaction, good performance, positive attitude, creativity, adaptability etc. are very important for the growth of any organization. Human Capital in an organization does not refer simply to the people working for an organization. It can be seen in terms of the total collective worth of the capabilities, skill-sets, prior-experiences, knowledge and motivation present in an organizational workforce. Human capital is of supreme importance in organizations mainly because of the huge cost that is incurred on them and also because of the fact that if properly guided, human capital has the power to take an organization to a much higher level. Going by the huge amount of literature available on this topic, it can be said that the idea of monitoring and guiding the performance of both, an individual and the organization as a whole, is not a new concept. The origin of performance man agement can be traced back to the period of First World War when the assessment used to have the personality of the individual as the criteria of assessment. During the 1960s, the appraisal practice shifted its focus towards the setting of organizational goals and assessing an individual on the basis of performance related competencies and not personality. The late years of 1980 and the whole of 1990 witnessed the organizations undergoing a lot of quick change. During this period, the appraisal system had become the core process of managing the workforce and the organization as a whole.

Brand Management - Create a new sub-brand for Ted Baker plc (new make Essay

Brand Management - Create a new sub-brand for Ted Baker plc (new make up range) - Essay Example bulences within the British and the international market, the firm needs to prepare in advance a careful plan for the relevant initiative estimating the effectiveness and the feasibility of this project in the long term. In order to design the plan related with a specific product, we should primarily examine its potential market in terms of the customers’ preferences as they have been observed in relation with similar products. In this context, it is suggested by Henderson et al. (2000, 112) that ‘the building of a brand starts with a precise definition of the target customer group and its needs and expectations and proceeds to a realistic assessment of how well the brand currently meets them’. From a similar point of view, Dennis et al. (2002, 355) noticed that ‘branding is well known for consumer products but power has shifted from manufacturers brands towards retailers while the term image is more common than brand in the context of shopping centres, but branding may become more important’. In accordance with the above, the design and the development of a brand can be a challenging task especially when the targeted market is a competitive one (like in the case of UK). The proposed product will include a series of sub-products. More specifically, the firm is interested in launching a series of make up products including: a make up crà ¨me, a make up powder, a mix – make up product (crà ¨me and powder in one product) and a compact make up product which will be characterized for its long duration. The careful examination of the British market – especially of the specific sector – showed that this market is offered for the specific product. More specifically, in accordance with a series of statistics published by Market Research Portal (2006) the value of British cosmetics market is estimated to approximately 1.5 billion Euros for 2006. On the other hand, it is noticed that ‘over the past five years the make-up market has seen vibrant growth of

Importance of Early Childhood Education Essay Example for Free

Importance of Early Childhood Education Essay With the economy currently in such a terrible position now it is’s no wonder why that people everywhere are being laid off, and companies around both the country and the world are jobs are laying off and choosing to keep staff to a minimum. Unfortunately, the positions that are kept are held by those who have the educational background that has essentially securedd their position from the beginning. There once was a time where an individual could apply for a job and their lack of educational experience was not held against them, but. N now, it seems as if there are extremely few reputable jobs are ooffered where only a high school diploma and some college is sufficient. But However, the economy is not who shouldto be blamed. The question instead that we should be asking that should be asked is, â€Å"where does it start? † Although it is understood that education is needed to surpass stay ahead throughout our lifeves, the importance of early education is frequently overlooked. Childhood education, as in Pre-Kindergarten thru fifth grade, is not about learning the simple or basic parts of education, but instead rather itsis about introducing young children’s minds to new and more complex things that will prepare them for a successful future. Some may choose to believe that pushing a child to learn a multitude of things at a young age would be considered cruel. Some may also believe that spending too much time learning would take away a child’s â€Å"childhood,† and that children should be allowed to stay children for as long as possible. , and should be allowed to play as much as possible. On theWhile the importance of imaginative play is alsovery important,contrary, if you were to analyzeing the circumstances it makes it would be clear as to why so many children have chosen a path of uncertainty in education. For example, take a five year old child who is being introduced to school for the first time. He or she would learn the basics; counting, coloring, reciting the ABC’s. Now take a child who has been introduced to a pre school program (normally around the age of four) as similar to that ofin First Step or Head Start. By the time that child will have entered into Kindergarten he/ or she will have already been introduced to a level of learning that is above any child that has not take advantage advantage of early learning programs. Thise will enable them to excel above their classmates. Children’s minds are truly like sponges; when a child is still young their mind’s this should be the time when their minds sshould be â€Å"crammed† with an abundance of knowledge. Sean Brotherson, a family science specialist, gives the example of a child’s brain being like a house that has just been built. He says, the walls are up and the doors are hung. You bring supplies to the new house and set them on the floor, but they will not work until everything is wired and hooked up. He goes on to explains, â€Å"wWhileexplain, â€Å"while learning later is possible, it usually is slower and more difficult. Some improvement in most skills is possible throughout life. However, providing children with the best opportunity for learning and growth during the periods when their minds are most ready to absorb new information is important†. From the time a child is born he/she is learning. A disinterest in learning at an early age is something that may follow him or her throughoutu their school age life, and is something that can haunt them for their entire lives and may hinder their ability to succeed in the real world. If a child shows no interest in adding and subtracting from the very beginning, it’s is often likely that child will continue thru school disliking Mmath throughout school. . and that This is something that can hold a child back from their potential. It also started at a young age. Children need to believe that learning is fun and, at the right age, is something that can help them progress farther. in life. In most Pre-Kindergarten classes in the United States, children are being taught to button their clothes, tie their shoes, or count to ten, and introduced to letters and shapes for the first time. These are all good tools to learn, just not in school. If you were to look at schools in other countries, they are far beyond what the U. S. is teaching our Preschoolers. Elaine Wu, with The Kapi’o Newspress, did an article on the failing education in the U. S. She found that according to a study done by UNICEF, the U. S. was failing when it came to international education rankings. Studies showed that other nations in the â€Å"developed† world had a more effective education systemsmore effective education systems. How wrong could it be to give our children a little more to think about, or giving them a problem that may take a little longer to solve?. Being that the children that attended my hHigh sSchool were approximately 75% mMilitary â€Å"brats,† it wasn’t not rare to have a new kid transfer in. There were kids from all places over the world but the ones that stood out were the ones from different countries. I took special interest in where they came from, but what interested me more was the fact that they were being placed into mostly advanceds classes. So with that curiosity I would start a conversation, usually beginning with â€Å"where are you from†? aAnd usually often times they were from another country or from somewhere where education was an important factor in their community. I am now 26 years old and am finally deciding to finish school. Things like what I just described makes me wonder about the course my life has taken. ifIf I had beenwere pushed into the books and encouraged to learn when I was younger would my outcome in school be different?. Here is another example. I look at my I have a fourteen year old nephew who is now in 9th grade. He is a straight A student and plays basketball (coincidently at my same high school). His grades are monitored by his parents but the transition wasn’t was not always easy. He came from New Orleans, Louisiana, where he lived with his mother. New , whichOrleans has one of the worst school systems in America so his early education was a struggle. When he moved to Hampton, trying to get him to open a book and study was next to impossible but. Wwith a lot of perseverance he now knows how important school is and where it can take you. But imagine if he had beenwas excited to learn from the beginning. If he had been given the opportunity, the transition may have been easier. I am now 26 years old and am finally deciding to finish school. Things like what I just described makes me wonder about the course my life has taken. If I had been pushed into the books and encouraged to learn when I was younger, would my outcome in school be different? I don’t expect society’s children to be building rocket ships at the age of eight. But putting more thought in to teaching children things that broaden their minds wouldn’t be such a bad thing. WWork Cited Brotherson, Sean. â€Å"Growth of the Mind†. Online posting. August 2001. www. nbsu. com Wu, Elaine. â€Å"Failing America†. Kapi’o Newspress. 25 April 2001 .

Stability and Control of Nonlinear Systems

Stability and Control of Nonlinear Systems The following system was provided to study about passivity, asymptotic stability, and input to state stability properties at conditions. The given system of differential equation for analysis is given below Also, The state space representation of the system is as follows. Let, Similarly Hence Based on the system equation is given by Similarly, based on the state space representation The state of system , where n=4, where m=2 and where p=2. Hence p=m.   A dissipative system with respect to supply rate is said to be passive, if The Lyapunov function for the system is given as Hence with respect to definition of state variables, it can be rewritten as Hence, Also, based on the definition of S, For the given system , hence the lossless system, is passive from u to y. The PD feedback controller of the system with and is represented as Hence the state space representation of the system, is given by The modified Lyapunov function with potential energy is given by It can be observed that V(x) is differentiable (V: R4 → R and a C1 function). Based on the equation of V(x) it can be observed that, The term and all other terms are quadratic in nature.   Hence where Hence V(x) is positive definite. Let V(x) is bounded by V(x)≠¤M, where M à Ã‚ µ R, then it implies that ⇒ ⇒ and ⇒ ⇒ and ⇒ ⇒ and ⇒ ⇒ and ⇒ ⇒ Hence V(x) is radially unbounded. The derivative of V(x) can be obtained as follows Upon substitution and solving the equations, At It can be observed that Based on the above equation, it can be observed that It can be observed that , has only quadratic terms with a negative sign prefixed hence where Hence is negative definite. The equilibrium point of the system at is given by Hence origin is the only equilibrium point of the system. Based on the above observations, it can be concluded that the system is globally asymptotically stable at the origin. The given systems were simulated for different values of   and , modified one at a time with other disturbance set to zero and the initial condition set at origin. The following observations can be found from subplots of and Hence the disturbance in both the coordinates of the system are additive in nature. It can be observed that, whenever initial state of the system set to origin and disturbance is induced in one of the coordinate ( or ), the other coordinate of the system is not disturbed. Figure 1 State of System with disturbance at origin with rd=0 Figure 2 State of System with disturbance at origin with thd=0 The Figure 3 indicates the state of the system, when is modified from -5 to 5 with , with the initial condition as x = {7,3,5,1}.   The settling time of the system varies with the magnitude of disturbance and the initial condition.   Also, it can be observed from the plot of that the system settles to a point which is offset from the origin (equilibrium) by the value of disturbance.   Also, the settling time of the system is more for d=-c, when compared to d=c. Also, disturbance in one of the coordinate (), has its effect in another coordinate. Figure 3 State of System with rd=0 at x = [7,3,5,1] The observations of disturbance induced in when , is applicable for the disturbance induced in with    Also, it can be observed from Figure 4 that the settling time of the system is higher when a disturbance is induced in r-coordinate, when compared to -coordinate. Figure 4 State of System with thd = 0 at x = [7,3,5,1] The effect of having both and was observed by simulating the system response for and . Also, it can be observed that settling time of the system is similar to disturbance induced only in the r-coordinate. Figure 5 State of System with thd = -5, rd=5 at x = [7,3,5,1] In all the above plots, it can be observed from the subplot of that the settling point of state as t→, and , indicating that the state of the system tracks the input in the respective coordinate. It can also be observed from the previous plots for d=0, system exhibits the property of global asymptotic stability to the origin (equilibrium point).   Also, , the state implies the Bounded Input Bounded State property of the system. The input to state stability of the closed loop system with respect to and for the system was validated by adding a destabilizing feedback with and . The function k(x) of the disturbance is selected, such that the power transferred to the system is maximized, which can be performed when   . From the above equation, it can be observed that the power transferred to the system can be maximized by choosing same sign of with c≠¥0.   The nature of system response for different range of c is listed in the Table 1 below. Table 1 System Response for Variation in c at initial condition of [7,3,5,1]] Value of c Observation c ≠¤ 1.99 The energy of the system decreases initially, indicated by the plot of Lyapunov function shown in Figure 6 and the same result can be observed on the plot of r and ÃŽ ¸, where the magnitude decreases initially and oscillates with the bounded magnitude, for the bounded input indicated in plot of theta-d. c>1.99 For c=5, the energy of the system increases, indicated by the plot of Lyapunov function shown in Figure 7 and the plot of r and ÃŽ ¸ indicates that the magnitude continues to increases resulting in unbounded state for the bounded input indicated in plot of theta-d.   Also, it can be observed that the rate of increase in energy of the system, decreases with time. Figure 6 State of System at c=1.75 The system is not Input to state stable (ISS) for c>1.99 and Figure 7 indicates a system which is not ISS for c=5.   The value of transition from bounded state to unbounded state was observed at c=1.93 for an initial point of [1,2,1,2].   Based on the above observation, the transition value of c is dependent of initial condition (energy) of the system. Figure 7 State of System at c=5 The PD control used in the r-coordinate is modified as The simulations were carried out, to identify the properties of ISS satisfied by the system, with respect to and as inputs.   All the simulations were carried out with respect to the initial condition x0 = (7,3,5,1) Condition 1: The system is evaluated with zero disturbance and , the result is indicated in Figure 8. Figure 8 System with Zero Disturbance For the no disturbance conditions, it can be observed that the system is asymptotically stable about the origin (equilibrium), indicating the Global asymptotic stability of the system about the origin.   Also from the plot of Lyapunov function, it can be observed that the energy of the system settles down to zero. Condition 2: The destabilizing feedback input used in question 5 for the system was fed to the system and it its response is indicated in figure Figure 9 State of System at c=5 with modified PD Control The following observations can be made with respect to figure For an input , the state , indicating bounded input bounded state property of the system. It can be observed that, though the energy of the system increases initially, but upper bounded over a period.   The energy and the state of the system gets bounded over period of simulation.   Hence for the bounded input, state of the system is bounded. Also, the system exhibits property of asymptotic gain, since the state of the system is upper bounded by disturbance with gain of the system. Also, it was observed that though the system is ISS for the c=5, as the value of c increases energy of the system increases (example for c=10, v(x) is upper bounded to 10,000).   Hence modifying the PD control, makes the system ISS for a larger range of disturbances, when compared to earlier control. Condition 3: The system was fed with the input Figure 10 State of system rd=0 and theta d=5*exp(t) It can be observed from the plot that d(t)→ 0 as , also aysmptotically.   Hence the system indicates the property of converging input, converging state. The response of the system was evaluated with different possible inputs for , such as , the state of the system x1, x3 was chosen based on observations made in earlier simulations (q5) where predominantly these states grew out of bound Similarly, the above input conditions were simulated with =0 and defined as one of the input, few combinations of the above input disturbances and few possible system interconnections such as positive feedback interconnection, negative feedback interconnection, series interconnection. System response for various types of disturbance Constant Disturbance The disturbance of the system is set to constant values, as indicated in Figure 5 Figure 11 State of system at theta d=-5 rd=5 It can be observed from the plot of Figure 11 and Figure 5 that the settling time of system in r-coordinate has reduced almost by half, when compared to previous control. Positive Feedback Interconnection The disturbance input condition is mentioned below and the system response is shown in Figure 12 Figure 12 System Response for Positive Feedback Interconnection The state of the system indicates the converging nature, also it can be observed that after the transient period system follows the input. Series Interconnection The system is connected in series, with the following disturbance input configuration for each of the subsystem and the plot for the same is shown in Figure 13. Figure 13 System Response for Series Interconnection It can be observed that the behavior of the system is similar with respect to condition 2, but the energy of the system settles down at a higher level when compared to the similar condition with System with different disturbances acting simultaneously The type of disturbance added to the system is given below and the response of the system is shown in figure Figure 14 System response of simultaneous time varying disturbance It can be observed that the system exhibit the property of bounded input bounded state, even if the disturbance is of time varying. In all the above simulation conditions, it was observed that the system exhibits bounded state nature for a wider range of inputs with higher magnitude, when compared to the PD control implemented earlier.   This phenomenon can be attributed to the cubic terms with the negative sign, as it can reduce the rate at which energy of the system increases, before it goes out of bound. APPENDIX Code Used for Generation of Plots Contents Q4 Constant Value of Theta-d and r-d Q5 for ISS Q6 for ISS with new u2 Q4 Constant Value of Theta-d and r-d clc clear all close all global x1d; global x3d; ts=500;   %Duration for solving ip=[7,3,5,1]; options=odeset(AbsTol,1e-7,RelTol,1e-5); thd=[-5]; rd=[5]; for i=1:size(thd,2)   Ã‚  Ã‚   for j=1:size(rd,2)%-29:30:31   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   x1d=thd(i);  Ã‚   %x1d is Theta-d   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   x3d=rd(j);  Ã‚   %x3d is r-d   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   [t,x]=ode23(@deeqn,[0 ts],ip,options);   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   figure(1)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,2,1)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,x(:,1))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of Theta)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(Theta)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,2,2)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,x(:,2))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of Theta-dot)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(Theta-dot)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,2,3)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,x(:,3))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of r)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(r)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,2,4)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,x(:,4))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of r-dot)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(r-dot)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor   Ã‚  Ã‚   end end Q5 for ISS clc close all global x1d; global x3d; ts=10000;   %Duration for solving ip=[7,3,5,1]; options=odeset(AbsTol,1e-7,RelTol,1e-5); x1=ip;    global c;    cval=[1.92]   %1.993 is transition point    for i=1:size(cval,2)    c=cval(i);   %4.0125   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   x1d=0;  Ã‚   %x1d is Theta-d   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   x3d=0;  Ã‚   %x3d is r-d   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   [t,x]=ode23(@deeqnvx,[0 ts],ip,options);   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   figure(2)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,3,1)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,x(:,1))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of Theta)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(Theta)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,3,2)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,x(:,2))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of Theta-dot)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(Theta-dot)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,3,4)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,x(:,3))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of r)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(r)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,3,5)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,x(:,4))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of r-dot)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(r-dot)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,3,3)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   thdin=c.*sign(x(:,2));   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,thdin)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of theta-d)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(theta-dot)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,3,6)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   vxfn=(1/2).*(((x(:,3).^2)+1).*(x(:,2).^2)+(x(:,4).^2)+(x(:,1).^2)+(x(:,3).^2));   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,vxfn)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot Lyapunov Function)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(v(x))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor    end Q6 for ISS with new u2 clc close all global x1d; global x3d; ts=100;   %Duration for solving ip=[7,3,5,1]; options=odeset(AbsTol,1e-7,RelTol,1e-5); x1=ip;    global c;    cval=[5]   %1.993 is transition point    for i=1:size(cval,2)    c=cval(i);   %4.0125   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   x1d=0;  Ã‚   %x1d is Theta-d   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   x3d=0;  Ã‚   %x3d is r-d   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   [t,x]=ode23(@deeqnr,[0 ts],ip,options);   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   figure(3)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,3,1)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,x(:,1))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of Theta)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(Theta)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,3,2)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,x(:,2))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of Theta-dot)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   xlabel(Time)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   ylabel(Theta-dot)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   grid minor   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   subplot(2,3,4)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hold on   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   plot(t,x(:,3))   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   title(Plot of r) Function for constant disturbance function dx = deeqn(t,x) % Function for system model %  Ã‚   Argument function for ODE Solver global x1d; global x3d; dx=[x(2); (-2*x(3)*x(4)*x(2)-x(2)-x(1)+x1d)/((x(3).^2)+1);x(4);x(3)*(x(2).^2)-x(4)-x(3)+x3d]; end System with Destabilizing Feedback function dx = deeqnvx(t,x) % Function for system model %  Ã‚   Argument function for ODE Solver global x1d; global x3d; global c; x1d=c.*sign((+1).*x(2));; dx=[x(2); (-2*x(3)*x(4)*x(2)-x(2)-x(1)+x1d)/((x(3).^2)+1); x(4); x(3)*(x(2).^2)-x(4)-x(3)+x3d]; end Function with new u2 and old u1 function dx = deeqnr(t,x) % Function for system model %  Ã‚   Argument function for ODE Solver global x1d; global x3d; global c; x1d=x(4);%c.*sign((+1).*x(2));; x3d=x(2); dx=[x(2); (-2*x(3)*x(4)*x(2)-x(2)-x(1)+x1d)/((x(3).^2)+1);x(4);x(3)*(x(2).^2)-x(4)-x(3)+x3d-(x(3).^3)+(x3d.^3)]; end Published with MATLAB ® R2016b

An Argument Against Abortion :: essays research papers

Psychologist, Sociologists and Anthropologist all have their place when it comes to analyzing criminals. Any member from these fields of study would have an interesting time analyzing Eric Rudolph. Eric Rudolph has been held responsible for numerous bombings of anti-abortion clinics as well as the bombing at the 1992 Atlanta summer games. As a result of these bombings one person has been killed and over 100 injured. From a psychological standpoint, Mr. Rudolph had some very serious issues. He was ruled to be mentally unstable. Eric belonged to a great number of anti-abortion groups. From a sociological stand point these groups did a great deal to aid in the crimes committed be Eric Rudolph. Upon looking at this case form the point of view of an anthropologist it becomes very clear that there is international support for Eric’s cause and very little being done to stop such actions.   Ã‚  Ã‚  Ã‚  Ã‚  Mentally unstable, odd, unsociable and depressed were all words used to describe Eric Rudolph in his childhood. In grade six a teacher wrote the following in the comment section. â€Å"Your son is an odd individual, please seek help for him. He is incredibly bright and gifted yet awkward and anti-social.† Eric Rudolph was constantly being called a failure and being told he was a plague on the planet, by his alcoholic father Mike Rudolph. Eric needed something to believe in, he took a stand in anti abortion. Anti abortion was a cause he supported ever since a very young age. Eric believed his criminal activities were simply moves in a direction of ending abortion and saving a life. This was his way of striving to make a difference. Time magazine did a psychological analysis of Eric Rudolph and they concluded â€Å"†¦ very little ability to decipher right from wrong, Eric is a young man that exploded in the very same manner as his bombs did. He was a cl ock waiting for the right time to make some noise.†   Ã‚  Ã‚  Ã‚  Ã‚  In the words of the great philosopher, David Letterman, â€Å"Eric Rudolph is like the whore of the anti abortion world, he is in bed with every anti abortion organization imaginable, some peaceful and some not so peaceful.† The role of a sociologist is to look at what groups or organizations that the person belongs too. There is a great deal of groups that Eric Rudolph was associated with. It was these groups that were responsible for teaching Eric what he needed to know in order to make a bomb.

Education: Empiricists vs Rationalists Essay example -- philosophy of

The importance of experience in education has always been the subject of philosophical debates. These debates between empiricists and rationalists have been going on for quite some time. Rationalists are of the view that knowledge acquired through senses is unreliable and learning can only be done through reasoning. On the other hand, empiricists believe knowledge is acquired through empirical impressions and concepts that cannot be learnt without being experienced (Evans, 1992, p. 35). This debate was however resolved by Kant who argues that both experience and rationality are necessary in learning. John Dewey was an American philosopher of the twentieth century and he also contributed to the debate on the learning process. In his book Experience and Education, Dewey (1938), he stated, â€Å"the belief that all genuine education comes about through experience does not mean that all experiences are genuinely or equally educative† (p. 25). This famous quote has been evaluated a nd referred to regularly in the debate on learning through experience. According to Dewey (1938) experiences can only be termed as educative if they lead to further intellectual and moral growth. In order for experience to be termed as educative, both the community and the individual have to benefit from the said experience. The experience has to contribute to growth in curiosity, sense of purpose, and initiative in the learner. He was of the view that traditional education was hierarchical and therefore undemocratic in nature. According to him, in order to produce well informed, thoughtful and democratic students, learners need to participate in all aspects of the school program and gain the experience. Eventually, a learner has to reflect on the experience ... ...work became widely accepted. However, his philosophy will continue to earn critical acclaim even in the coming days. Works Cited Boud, D., Keogh, R., & Walker, D. (1985). Reflection: Turning experience into learning. New York, NY: Kogan Page. Dewey, J. (1938). Experience and education. New York, NY: Macmillan. Evans, N. (1992). Experiential learning: Assessment and accreditation. New York, NY: Routledge. Ewert, A. (1989). Outdoor adventure pursuits: Foundations, models, and theories. Columbus, OH: Publishing Horizons. Goodlad, J. (1984). A place called school: Prospects for the future. New York, NY: McGraw- Hill. Kolb, A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall. Lewin, K. (1952). Field theory in the social sciences: Selected theoretical papers. London: Tavistock.

What Influences Free Clinic Usage by the Uninsured

What Influences Free Clinic Usage by the Uninsured? By Shelli Thomason A Paper Submitted to Dr. Dayna McDaniel Research Methods PA6601 Term 5, 2012 Troy University July 27, 2012 TABLE OF CONTENTS CHAPTER 1 Introduction †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 4 Statement of the Problem†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 5 2. 1 Purpose †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 6 2. 2 Problem Statement†¦.. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 6 2. 3 Research Questions†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚ ¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 6 2. 4 Scope†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 1. Literature Review†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 9 Dependent variable†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 9 1st Independent variable†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 11 2nd Independent variable†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 13 3rd Independent variable†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢ € ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 14 4th Independent variable†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 16 4Hypothesis†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦18 4. 1 H1: hypothesis one†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 18 4. 2 H2: hypothesis two†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 18 4. 3 H3: hypothesis three†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦18 4. H4: hypothesis four†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 18 Chapter II: Methodology Design†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. †¦ 18 Population/Sample†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 20 Variables†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦21 Dependent Variable†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢ € ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 21 Independent Variables†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. †¦Ã¢â‚¬ ¦. †¦Ã¢â‚¬ ¦22 Data Collection†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. †¦22 Measuring Instrument†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ †¦Ã¢â‚¬ ¦. 22 Materials†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 23 Delivery Method†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã ¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 24Data Analysis†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. †¦ 24 Chapter III: Anticipated Findings†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 25 Chapter IV: Conclusion†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦25 Implications†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦26 Recommendations†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦26 References†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦28 – 30 Appendices Appendix A Schematic Model†¦. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ †¦.. 31 Appendix B Formula for Calculating Population Sample Size†¦. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 32 Appendix C Survey†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. †¦Ã¢â‚¬ ¦ †¦ 33 – 35 Appendix D Demographics†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦36 Appendix E Example of Multiple Regression results†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦37 Chapter 1 Introduction Many United States residents delay or do without necessary healthcare because they lack the resources or knowledge to access it. There are 46 million people in the nation who have no health care coverage, and by not giving necessary attention to medical concerns and conditions, poor health risks increase, along with untimely mortality (Darnell, 2010).A Kaiser Commission study from 2006 identifies there are 18,000 deaths yearly in the United States resulting from lack of health care coverage (Trask, 2011). Recent Census Bureau shows a slightly highe r number of uninsured indicating there are 50 million uninsured, which would be the largest number on record, resulting from the national economic recession (Krisberg, 2010). According to Darnell (2010), there are 1007 free clinics in the nation, providing services during 3. 5 million clinic visits, by 1. 8 million uninsured patients, representing approximately 10% of uninsured adults of working age.The patients have no other health care alternatives to a free clinic due to a variety of factors including: no ability to pay, language barriers, lack of or inadequate medical insurance, homelessness, inaccessibility, and immigration or ethnicity issues. As private non-profit organizations, free-clinics are not recipients of federal funding, so many rely on state funding, local funding, and donations. Depaul (2010) notes that the National Association of Free Clinics estimated four million patients were seen in 2008, which doubled in 2009.It is also noted that free clinics have to turn aw ay patients because they cannot meet the demands. In a white paper for the American College of Physicians, Gorman (2004) notes, those who do not receive annual exams and preventative screenings run the risk of a delayed diagnosis and subsequent treatment, resulting in premature mortality. Additionally, untreated chronic symptoms result in worsened conditions and costly emergency care, placing a financial burden on hospitals, families and ultimately on the community. Furthermore, workers who experience poor health have lower productivity which is costly to the economy.Therefore, free clinics are a crucial component in the consortium of health care options in the United States. Isaacs and Jellinek (2007), state that 80 % of patients who receive primary care at a physician’s office are either uninsured or have Medicaid. Although physicians may see uninsured patients in their offices and take on a few of them as charitable cases, this practice is declining given lower insurance a nd Medicaid reimbursements and increased operational expenses. The nation has what is referred to as a safety net system to provide health care services for residents who are uninsured.This system is comprised of hospital emergency rooms, publicly funded health centers, and free clinics. With costs of health care escalating, it is crucial to identify methods to effectively optimize these providers. It has been suggested that accessibility to free clinics, which may keep the uninsured from accessing the ER for non-emergent care, is one such method. Studies show uninsured persons utilizing a free clinic have fewer emergency room visits than those who do frequent the ER for their primary care, which renders cost savings (Trask, 2011). Statement of the Problem PurposeThe purpose in this research is to make determinations as to what factors influence an uninsured person’s decision to access the services of a free clinic. In an effort to answer this question, factors will be recogn ized, through research, significant to a person making the decision to visit a free clinic for medical care. Uncovering these factors could assist in discouraging the misuse of other types of medical safety net provisions. One study shows if the group studied did not have use of a free clinic, 80% of the visits would have resulted in ER visits for non-emergency treatments (Corso & Fertig, 2011).This information could also assist in identifying strategies to effectively address the health care needs of constituents and provide funding sources with knowledge to make educated decisions on the most effective use of funds. Problem Statement This project will pinpoint the most acute variables influencing an uninsured person to seek treatment at a free health clinic, allowing local government leaders and medical providers to have access to research so they may further understand areas in which to place their focus and funding.Furthermore, an ancillary reason for study is to show that by pr oviding an uninsured person who is truly ill with a way to achieve wellness, they can become viable again, thus becoming a more productive worker, who may regain insurance and no longer need the free service, or any other type of medical care. If a person has a resource within which to address health concerns, that does not present them with barriers, they are likely to receive the necessary care needed, reducing further complications and costs, placing them in a position to become more sustainable.In one Healthcare Georgia study, evidence shows that free clinics can halt the escalation of health problems, reducing or eliminating the need for hospitalization (Corso & Fertig, 2011). Research questions This project will focus on four research questions that will aide in identifying specific factors that influence an uninsured person to use a free clinic (dependent variable). The primary question to be asked is â€Å"What factors influence an uninsured person to use a free clinic? Res earch questions inquiring about those influences (independent variables) are: 1) Does lack of alternative health care options influence an uninsured person to use a free clinic? 2) Does housing status influence an uninsured person to use a free clinic? 3) Does Hispanic ethnicity influence an uninsured person to use a free clinic? 4) Does age influence an uninsured person to use a free clinic? The independent variables thought to influence the dependent variable are defined so there is a clear understanding of their meaning.Lack of other alternatives: Many users of free clinics may have no other options for health care than a free clinic. They may be employed, but cannot afford the health care premiums offered by their employer or the employer does not offer health coverage. 83 percent of the patients seen at free clinics come from a working household and may hold two or three part time jobs (DePaul, 2010). Federally funded community health centers, different from free clinics, are t ypically located in rural or inner-city areas and help serve a large number of patients in high-needs communities.In 2009, the Government Accountability Office indicated that even with 8000 community health centers, there were still 43 percent of underserved areas without access (Whelan, 2010). Housing Status: The definition of â€Å"homeless† is a broader scope than merely the population living on the streets and includes individuals in a widespread range of unstable housing scenarios. Homeless individuals do not only live under bridges or in a car, but may also reside in emergency shelters; foster homes; HUD’s terminology of â€Å"doubling up† with relatives or friends; or tenants who have been served an eviction notice.Unstable housing status is a high risk factor for health disparities, much like genetics or eating habits. On average, a homeless person has eight to nine coexisting health problems (Batra et al. , 2009). A study of 6,308 homeless Philadelphian s determined the mortality rate among the homeless was 3. 5 times that of the city’s overall population. Earlier research has also noted the homeless have escalated rates of a vast array of health problems (Lewis, Andersen and Gelberg, 2003). Age: Different clinics have differing eligibility for the patients they serve.Many states have the option to offer an insurance plan covering children through the passage of the Children’s Health Insurance Program Reauthorization Act (Llano, 2011), then those over age 65 have Medicare. Therefore many clinics tend to turn their efforts toward those uninsured patients between the ages of 18-64. A 2004 study shows that overall general health significantly declines for those between age 50 and 60 if they are uninsured, underinsured or sporadically insured, compared to their counterparts who have adequate health coverage (Inguanzo and Kaplan, 2011).Hispanic Ethnicity: Llano (2011) states the greatest hindrance to health care for Hispan ics is the language barrier. Providers of service have difficulty communicating with Spanish speaking patients if there is no interpreter available, which may cause compromised diagnoses, treatment options and specialty referrals. Census Bureau data reveals that in 2010, 38. 7 percent of uninsured American residents were Hispanic (Inguanzo & Kaplan, 2011). Scope A survey will be completed, as part of this research. This project’s scope will investigate what influences an uninsured person’s visit to a free clinic.It will assist the free clinic administration in further developing strategic plans to make determinations on where their efforts should be focused. It may also contribute to local governments and other potential grantor’s decisions on making allocations. Free clinic usage is the primary focus, although the collective information may show related trends and concerns constructive to area healthcare providers and local governments. Each person surveyed wil l be treated equally. This study’s sample population will include patients of two free clinics: Community of Hope Health Clinic and Cahaba Valley Health Care Clinic in Shelby County, Alabama.The clinic only sees uninsured patients on Mondays from 8:30 am to 4:30 pm and Thursdays from 5:30 pm to 8:30 pm. They must show proof of residency in Shelby County. Literature Review Dependent variable: Free clinic usage by the uninsured As stated earlier, experts concur that there are over 1000 free clinics in the nation, providing services during 3. 5 million clinic visits, by approximately 10% of uninsured adults of working age (Darnell, 2010; Gertz, Frank and Blixen, 2010; George Washington University Report to Congress, 2012).This equates to approximately 90% of uninsured adults who are not utilizing a free clinic for their medical needs. Gertz, Frank and Blixen (2010) go further to say that since 1980, when there were 30 million uninsured people, there has been a 50% increase to 45 million. From a statewide perspective, Rhode Island remains consistent with national levels, as uninsured working age adults under age 64 doubled between 2000 and 2005, citing the waning of employer health care coverage (Gerber, et al. , 2008). The yearly cost associated with uncompensated medical treatment for the uninsured in the nation was $56 million in 2008.Determinations were made to suggest that use of emergency rooms for non-emergent care, along with rising hospitalization which could have been prevented are on the rise and creating costly problems. Communities are seeking other solutions to provide health care to the uninsured, which might include free clinics, mobile clinics, and church and school sites to administer treatment (Fertig, A. , Corso, P. & Balasubramaniam, D. , 2011). As stated earlier, free clinics are an important part of the United States health safety net, serving mainly the uninsured, working poor.Historically, given minimal resources and relying on volu nteer health care providers, free clinics have focused on gap filling, temporary solutions to the population's health problems. Implementing a new paradigm, free clinics are now making disease prevention and health promotion a top priority (Scariarti & Williams, 2007). A nationwide cross-sectional study using a survey was conducted by Gertz, Frank and Blixen (2010) which they compared to the only other known published study of its kind by Nadkarni, et. al from 2005 to determine free clinic characteristics.Both studies revealed a mean of between 4,000 and 6,000 uninsured visits to the free clinics annually, and a third study agrees that most (67%) are located in the Southern region of the United States (Gertz, Frank & Blixen, 2010; George Washington University Report to Congress, 2012). Additionally, 77% of the respondents of the Gertz, Frank and Blixen study (2010) indicated the level of care received at free clinics was superior to prior medical care received, and 24% indicated if there was no free clinic available, they would not seek care, mainly due to cost.A high number of free clinics seem to function as a fixed source of medical care for their patients. The majority of free clinics describe the service they provide to their patients as continuing, 20 percent indicate the care as recurrent, and 5 percent depicted the care as irregular, only seeing a patient once (George Washington University Report to Congress, 2012).In contrast, prior to the recent national economic recession, a study associated with the utilization of three Massachusetts free clinics was conducted to determine what factors influenced people to use the free clinics, when it appeared there were a variety of ample options for medical care irrespective of health care coverage or income level. Although the study unveiled the three free clinics saw patients who had insurance, 81% of the respondents were uninsured (Keis, DeGeus, Cashman & Savageau, 2004).Lack of health care coverage, is the s ixth-leading cause of death, equating to 18,000 deaths annually for adults between the ages of 25 and 64 (Groman, 2004). The uninsured person may encounter severe financial and wellness obstacles, limiting their ability to obtain medical care and many times become indebted and more ill, as a result. A study conducted by Becker (2001) found that not only did uninsured persons with chronic health conditions lack adequate health care; their illnesses were also inadequately managed.Other findings were that with deficiencies of education regarding their health, those persons who are uninsured lacked the information, understanding, and resources that would allow them to manage their illnesses more effectively. Many uninsured patients can pay more than double the cost if they are forced to use a hospital for their care, due to the inability for price leveraging that medical insurance providers can afford (Groman, 2004). 1st independent variable: Lack of other optionsThe National Associatio n of Free Clinics indicates they see patients they never thought would come to a free clinic, with 83% of free clinic patients come from working home, but cannot afford COBRA if they have lost a job and are now working several part time jobs. Patients have reported they would likely go the ER or not seek care if they did not have access to a free clinic (Depaul, 2010). Private practice doctors are the primary source of health care for the uninsured, mainly because, historically, they have been plentiful in numbers, with 720,000 providing care according to Isaacs & Jellinek (2007).A second expert (Groman, R. 2004), agrees that free care by physicians is decreasing, which will greatly impact the medical safety net with growing numbers of uninsured. As stated earlier, the decline is largely the result of higher operating costs and inadequate Medicare reimbursement rates, prohibiting the doctors from being able to treat those who cannot pay (Isaacs & Jellinek, 2007). Even though charity from practicing physicians plays a vital role in treating the uninsured, they are not stand-ins for health insurance. Because of revisions to financing and rganization of medical care systems, doctors indicate in a New York Academy of Medicine study, they are unable to provide the same class of care to the uninsured, as they provide to patients who have health care coverage (Groman, R. , 2004). A recent report to Congress indicates that free clinics overall see millions of uninsured persons who may not achieve any level of care elsewhere. One study highlighted in the report revealed four main reason listed in order of percentage, people use a free clinic are: no health insurance (82%), referrals by others (59%), medications (38%), and no knowledge of where else to go (34%).The report also states that three quarters of free clinic patients do not have a regular method of care except the free clinic or the ER, suggesting free clinics fill voids, offering services not available (or ea sily reached) somewhere else (George Washington University Report to Congress, 2012). The Keis, et al. (2004) study is in accord with the report to Congress in that one-third of survey respondent gave their reason for using a free clinic as not knowing where else to go to receive medical attention.Another one-third cited lack of transportation, long wait times, finding child care or inability to leave work as the primary reasons they could not use other types of medical providers and instead sought treatment at a free clinic. As already learned, access to local safety net providers has limits to readiness in other ways as well. For example, in Jeffrey Trask’s unpublished dissertation (2011), he cites and agrees with the Keis study stating that other than the emergency room, many safety net providers aren’t open in the evenings or are scarce, so due to the need to work, a patient’s only option may be a free clinic open in the evenings.Likewise, clients of free cl inics forego after care or specialty care only a hospital can offer due to costs. Trask (2011) gives the example, when an uninsured person using a free clinic needs additional services outside the free clinic’s scope of care, sometimes old or bad debt is a major obstacle to receiving necessary treatment. Finally, options are limited for people who are not legally residing in the country. A collective characteristic of a free clinic is capacity to treat any patient without documentation regarding immigration status (Keis 2004).In a 2010 national survey, a census, the first of its kind in 40 years, 764 clinics were deemed eligible out of 1188 surveys mailed. A finding from the study uncovered that free clinics are a more important aspect of the national safety net, especially in the area of ambulatory care that originally thought. However, only 188 of the clinics surveyed offered all-inclusive services, and the survey concluded that a free clinic is not a replacement for compre hensive primary care (Darnell, 2010). 2nd independent variable: Hispanic ethnicity Hispanic persons comprise approximately 16 percent of the population in the U.S. but make up 25 percent of free clinic patients. Experts agree that unbalanced degree of Hispanic patients in free clinics indicates higher rates of lack of health care coverage among this group (George Washington University Report to Congress, 2012; Isaacs & Jellinek, 2007), with the latter authors citing an example from a Racine, Wisconsin clinic who had a one percent Hispanic patient base in late 1980s and a 50 percent Hispanic patients in 2006. Results were compared from two student-run free clinic studies on clinic characteristics and concurred that most of the patients were minorities.One study of 59 clinics reported that 31% of the patients seen were Hispanic, while the other study of 39 clinics revealed 53% of patients were Hispanic. The student run clinics demographic is quite different from non-student run clinic who report a client base of mainly non-Hispanic people (Gertz, Frank & Blixen, 2010). Studies indicate that Hispanic persons are more likely than non-Hispanics to fail to complete the Medicaid application and miss important dates for submitting required documentation.Furthermore, 43 percent of Hispanics who speak Spanish had communication problems with physicians compared to 16 percent of Caucasians; and non-English speakers had more difficulty in comprehending doctor orders (Llano, 2011). Because of non-existent health insurance and consequently no immunizations, a considerable outbreak of rubella plagued a Hispanic community in New York in the late 90s. The outbreak spread to adjacent communities and those with insurance were just as affected. In communities with high numbers of uninsured residents, it becomes more ifficult to provide disease control, and medical personnel have fewer opportunities to identify early onset of outbreaks, hampering containment efforts (Groman, 2004). In a report examining the unmet medical needs of the nation’s Latino population conducted by the American College of Physicians and the American Society of Internal Medicine, it was discovered that uninsured women had twice the likelihood as their non-Latino peers to be diagnosed with breast cancer in the later stages and uninsured Latino men were four times as likely to receive a prostate cancer diagnosis compared to non-Latino men.It is suggested that Hispanic and Latino immigrants are very unlikely to have the ability to access health care services due to governmental restrictions of the Personal Responsibility and Work Opportunity Reconciliation Act of 1996, and fear that their citizenship opportunities will be compromised by attempting to secure public aid assistance (Inguanzo and Kaplan, 2011). 3rd independent variable: Homelessness According to Wilson (2009), there are close to 800,000 homeless people in the nation, many of which have multiple disorders to include ast hma, nutritional deficiencies, skin infections, wounds, and diabetes, to name a few.Wilson’s and O’Connell’s research goes on to say that the homeless person’s ailments which are largely left untreated and worsen, lead to devastating illness. The mortality rate is excessively high in the homeless populace. O’Connell (2005) agrees with Wilson’s conclusions with regard to high mortality rates, and that homeless people are three to four times more likely to die than the general population. The risk is greatly increased in those homeless persons between the ages of 18 and 54, and that younger homeless women are four to 31 times more likely to die than their housed counterparts.Life expectancy in the general population is 78 years of age, and falls to between 42 and 52 years of age for the homeless population (O’Connell, 2005). Approximately 9 to 15% of the US population becomes homeless during their lifetime. Those who are truly without a place to stay and are considered literally homeless may be included in this figure, although the homeless are transient and in and out of shelters. Additionally, this figure may include those who HUD calls â€Å"doubled up† or â€Å"couch-homeless†. Other developed countries have a lower rate of this ategory of homelessness than the United States (Hoback and Anderson, n. d. ). For the U. S. overall in 2000, the estimate is 1. 65% of the population is â€Å"couch-homeless† (Census Bureau, 2000). One study highlights the Columbia-Harlem Homeless Medical Partnership (CHHMP), a free clinic run by students, that targets Manhattan’s homeless, providing medical students with a service learning opportunity and simultaneously, providing a medical home for homeless patients. Free student-run clinics are an integral piece of the medical safety net.In these learning settings, the requirements of medical students and in-need patients transect with the outcome of qual ity medical care. The disordered lifestyle of the homeless patient requires outreach to this population and a need for relationship building. This type of need is not feasible in the medical school setting but can be met at a student-run free clinic. Students are able to deal with the human side of public health disparity and learn more about other services and make referrals that can assist the whole patient, such as housing, health screenings, mental health providers, etc. (Batra, et al. , 2009).In congruency with the independent variable of other options stated earlier, an interview study of 2578 homeless and sporadically housed persons indicated that housing instability, abuse, multiple arrests, physical and mental conditions, as well as substance abuse were contributing forces to causing heightened usage of emergency rooms with a trial study group revealing on average seven visits per year. Galwankar (2004) and Whitbeck (2009) both conducted studies which emphasized the need to decrease emergency room use among the homeless populations, by focusing on identified risk factors from a public health standpoint (Galwankar, 2004).A large percentage of the homeless use hospital emergency departments for their primary care, even though it is not the most effective method of medical care for them, as it cannot provide continuity. Additionally, for hospitals and governments it is not cost effective (Whitbeck, 2009). Independent variable: Age Eighty percent of free clinic patients are between the ages of 18-64; with 12% being children and elderly being eight percent (George Washington University Report to Congress, 2012). Two pieces of literature agree with he statistic that one in every six people ages 51 to 61 partaking in the National Academies Health and Retirement Survey who were at the start of the survey, uninsured, developed a new finding of stroke, cancer or heart disease, over the next six year period (Institute of Medicine, 2012; Inguanzo & Kaplan, 2011). In agreement with an IOM report cited, a national trend study from 2007, looking at 10,088 uninsured older working age adults, found that this group is less likely to receive regular preventative screenings for breast cancer, prostate cancer and cholesterol that those with insurance in the same age group.Additionally, women who are uninsured or are on Medicaid have a more advanced stage of breast cancer at first diagnosis and lower survival rate than their counterparts who have private health coverage (Gerber, et al. , 2008). In a 2009 Kaiser report, 30 percent of people between the ages of 19 and 29, are uninsured, the highest proportion of any age group. Though the majority of these young adults are working, they experience lower pay scales, and often find health coverage too expensive for their budget.Most people in this age group reported they were in good health, but 10 percent indicated they were in poor or fair health; twice as many as those with medical insurance (Weaver, 2 010). Now, in 2012, many of this age group, because of provisions under the Affordable Care Act, will now be able to remain a dependent on their parent’s insurance policy until age 26, thus likely reducing the high percentage of uninsured in this age group (The White House, 2010). The number of children nationwide with no healthcare coverage is on the rise, but the impact from being uninsured on a child’s health has not been heavily explored.According to a Journal of Public Health article, in 2006 over one million children became uninsured, raising the total to 9. 4 million, or 12. 1% of all children in the United States. The spike in numbers can be credited to decreases in employer health coverage without corresponding growths in support provided by Medicaid or the State Children's Health Insurance Program (SCHIP) (Abdullah, 2010). One study analyzed information from more than 23 million children, under age 18, in the United States, using two large patient databases, to evaluate the effect of health care coverage status on pediatric hospital stays.The study resulted in findings that the rate of death for children who were uninsured was over 37 percent of the deaths studied (Abdullah, 2010). Hypotheses H1: The fewer options for medical treatment will influence an uninsured person to use a free clinic for health care. The more alternative options for medical treatment will influence less free clinic usage by an uninsured person. Other options is an independent variable that has a direct relationship with the dependent variable of free clinic usage by the uninsured.H2: Hispanic ethnicity will influence an uninsured person to use a free clinic for their medical care needs. Hispanic ethnicity will not influence an uninsured person to use a free clinic for their medical care needs. Hispanic ethnicity is an independent variable that has a direct relationship with the dependent variable of free clinic usage by the uninsured. H3: Homelessness will influe nce a person to visit a free clinic. Homelessness will not influence a person to visit a free clinic. Homelessness is an independent variable that has a direct relationship with the dependent variable of free clinic usage by the uninsured.H4: Age is a factor that influences free clinic usage by the uninsured. Age does not influence free clinic usage by the uninsured. Age is an independent variable that has an inverse relationship with the dependent variable of free clinic usage by the uninsured. Chapter II: Methodology Design This study will concentrate on one central research question: What impacts do availability of other medical care options, Hispanic ethnicity, homelessness and age have on the usage of a free clinic by people who are uninsured?These questions will pose the following hypotheses: H1: The fewer options for medical treatment will influence an uninsured person to use a free clinic for health care. The more alternative options for medical treatment will influence less free clinic usage by an uninsured person. Access to other options is an independent variable that has a direct relationship with the dependent variable of free clinic usage by the uninsured. H2: Hispanic ethnicity will influence an uninsured person to use a free clinic for their medical care needs.Hispanic ethnicity will not influence an uninsured person to use a free clinic for their medical care needs. Hispanic ethnicity is an independent variable that has a direct relationship with the dependent variable of free clinic usage by the uninsured. H3: Homelessness will influence a person to visit a free clinic. Homelessness will not influence a person to visit a free clinic. Homelessness is an independent variable that has a direct relationship with the dependent variable of free clinic usage by the uninsured.H4: Age is a factor that influences free clinic usage by the uninsured. Age does not influence free clinic usage by the uninsured. Age is an independent variable that has an inv erse relationship with the dependent variable of free clinic usage by the uninsured. A schematic model illustrates the correlation between these variables. The model can be reviewed in Appendix A. The research question and problem will be answered by using a survey design study conducted by a convenience sample over a six week period.The reason behind using a cross-sectional design is that data on all variables of interest can be collected at the same time and is an efficient method for a large group (O’Sullivan, Rassel & Berner, 2008). The three page survey, written at a fifth grade level, in English and in Spanish, will make inquiries and gather information about the independent variables, and about the dependent variable. Attempts will be made to approach every patient signed in at the clinics during the study period. Internal and external validity, then, are important to maintain when surveying a sample population and asking questions on sensitive issues.The goal is to en sure that the independent variables of interest indeed caused changes to the dependent variable and not something else; along with certifying the outcomes are general of the population and can be reproduced in any location. The development and reliability of the research questions are integral to maintaining internal validity within the study. Cognitive pretesting of 10 patients will be performed before beginning the study to ensure the questions are commonly understood and to confirm that the survey questions are capturing the intended outcomes.Additionally, in order to ensure external validity, the results of the study can be implemented by other governments and non-profit agencies. Population/Sample The population for this study is patients visiting two free clinics in Shelby County, Alabama, ages 19-64. This limits the population to a specific age range of persons in the county, as it has been determined that those outside this age range are eligible for coverage through governm ent offered insurance programs, even if they have not applied for it.A Shelby County Development Services Department Profile indicates from 2010 Census data; the population for Shelby County, Alabama is 195,084 residents. Of those approximately 7% are uninsured, equating to around 10,000 uninsured residents. County demographics reveal an almost even division of males (49. 3%) to females (50. 7%). 83. 6% of the population is white, 10. 6% is Black/African American and 1. 5% is Asian (See Appendix D). An anomaly in demographics is observed in ethnicity, specifically Hispanic/Latino residents who are documented at 4. % (8,389) of the total population with an additional 4. 2% who ‘speak non-English language at home’ and 1. 6% who ‘speak English less than ‘very well. ’ If the results of a University of Alabama at Birmingham study are applied to undocumented Hispanics in Shelby County, the total would be more accurately reported at 37,314 (Patino, 2002). Gi ven the fact that both clinics have eligibility requirement for the patients they see, the sampling frame will include only people ages 19-64, who have no insurance and who reside in Shelby County or indicate they are homeless.The sample will consist of those who randomly visit the clinic, and are signed in on a first come, first served basis and are waiting to receive treatment at the clinics during the study period, representative of the near 2000 patients who actually received treatment in 2011. This total number of patients is captured from clinic data gathered and reported by the clinics. The sample will be chosen through convenience sampling methods. This method was chosen for its ease of execution and cost effectiveness, although it has a higher risk of bias.The sample size was chosen using a formula that calculated a 95 percent confidence level that the sample size will accurately represent the total population of patients. The sample size will be 563 patients. See Appendix B. Variables Dependent Variable For this study, a free clinic is operationally defined as being a privately run non-profit agency not receiving any federal funding, that offers general medical services, medication and dental care to individuals who have no health care coverage. Volunteer, licensed medical providers administer the care at minimal or no cost (Darnell, 2010).The dependent variable is measured using nominal scales, with letters of the alphabet used as labels instead of numerals. Questions in the survey that address the dependent variable specifically are Question 4 and Questions 9-13 (see Appendix C). Independent Variables The first independent variable: lack of other options, can be conceptually defined as locations where the uninsured might seek medical treatment, other than a free clinic. To measure this variable, use of other options will be measured using a series of questions asking questions related to medical care history.Since the survey will be given to uninsu red patients who may not have a high level of education, literacy, or understanding of terminology, the operational definition for the second independent variable of housing status in the survey will measure living arrangements. This will be accomplished by measuring the frequency of responses using nominal scales. The third independent variable, ethnicity, especially Hispanic ethnicity, has been defined as being of Hispanic origin. Per the US Census Bureau, persons of Hispanic origin are determined on the basis of question that asked for self-identification of the person's origin or descent.Persons of Hispanic origin, in particular, are those who indicated that their origin was Mexican-American, Chicano, Mexican, Mexicano, Puerto Rican, Cuban, Central or South American, or other Hispanic (U. S. Census Bureau). The fourth and final independent variable, used in this model is age, and is intended to measure which age groups of working age adults visit a free clinic most often; and if age is a factor for visiting the clinic. In the study, variable is operationally defined as working age adults between the ages of 19-64.Free clinics trends have shown most patients are non-elderly adults (Darnell, 2010). This will be accomplished by measuring the frequency of responses using nominal scales. Data Collection Measuring Instrument The use of free clinics by the uninsured between ages of 19-64 and the relationships of the factors that influence usage, will be gauged by using a survey comprised of 20 questions (Appendix C), consisting of issues related to accessibility, reasons for use, medical insurance status, health status, employment status, housing status, current diagnoses, and general demographic information.These questions include both ordinal and nominal scales. Two questions will provide an open-ended answer option where space will be provided to write in an answer. Some questions for the survey were extracted from previously tested and validated instruments, such as the National Health Interview Survey. The survey will be translated into Spanish, and for those who need assistance, an already on-site Spanish interpreter will assist in the introduction of the study as well as offer explanation for completion of the survey.The survey should take no longer than 10 minutes to complete. Materials The materials and expense necessary to execute the survey are marginal. Copies required for each respondent total 4 pages (one page is the introduction and confidentiality notice and three pages for the survey) each totaling 2252 multiplied by $. 05 equals approximately $112. 60. Office supplies including three dozen writing pens and a stapler and staples will also be purchased for around $25. 00. Additionally, incentives in the form of refreshments are an additional cost.Bottled water and healthy snacks such as granola bars, pretzels or crackers will be purchased in volume to reduce costs. 25 cases of water totals $180. 00 and snacks will be approxi mately $150. 00. Therefore the total cost to administer the survey with incentive is approximately $467. 60. The study will be given during clinic operating hours where clinic volunteers will be recruited to administer the introduction and surveys providing additional cost savings. Delivery Method In order to allow every patient in the convenience sample the same opportunity to participate in the survey, upon their arrival and egistration, a clinic caseworker will share with them a scripted introduction explaining the purpose for the survey and assure them it is voluntary and it will in no way cause them any risk and will in no way compromise their clinic visit nor treatment. The introduction will also discuss confidentiality. These measures will help to ensure internal validity since the orientation may provide a level of comfort for the respondent who in turn may be inclined to answer the questions more honestly.The survey will be administered to the patients during regular clinic hours on Mondays between 8:30 am and 4:30 pm and Thursdays between 5:30 pm and 8:30 pm, while they wait to be seen. To improve response rates, healthy refreshments will be provided to participants. Patients who have been waiting to register for hours, to be one of 30 patients seen during a given clinic, have likely not eaten and may welcome refreshment as incentive to participate in the study. Dr.Eleanor Singer, a population studies professor and researcher at Columbia University summarized the evidence on incentives from the standpoint of the survey literature in the use of incentives in her 2002 book. She uncovered that incentives improve response rates across all approaches. The effect has proven to be undeviating, larger incentives have superior effects on response rates. Those patients who are first in line to see a medical provider will have equal opportunity to participate in the incentive and the study upon completion of their visit. Data AnalysisOnce the surveys are collec ted the data will first be cleaned. It is very important that the data collected from the surveys be able to be interpreted properly in order to accurately measure the relationships between the dependent and independent variables. Each question on the survey will be coded with a value prior to being administered. Data will be entered into a SDSS program and a multiple aggression analysis will be performed. From this analysis it will be possible to find the correlating relationships between each individual independent variable and the dependent variable to show significance.Ultimately the computer program will show which factors strongly influence free clinic usage, which ones are less influential and which factors together may increase the relationship further. See the example in Appendix E. Chapter III: Anticipated Findings The literature that has been reviewed in relation to the variables in this study, along with the suggested approaches, in tandem offers backing to the outcomes that are expected of this study.It is anticipated that there will be a relationship between use of a free clinic by the uninsured and each of the four independent variables provided: lack of other options for health care, age, Hispanic ethnicity and homelessness. The expectation is that the computer software used in analyzing the findings will show relationships between the variables, contradicting the null hypotheses. A multiple aggression analysis would be used to show these relationships by entering the data into a computer program designed to perform the computations and ends up showing a prototype of realism (Simon, 2003).Each of the four independent variables, are believed to have direct relationships with the dependent variable. Ultimately, it is anticipated that each of the four corresponding hypotheses will be conclusive. Chapter IV: Conclusion Studies provide support for the need to address reasoning behind free clinic usage by the uninsured population. The literature revi ew has assisted in understanding each variable’s definition, emphasizing the ideas and findings of other scholarly studies, and establishing the integrity of the links between each independent variable and the dependent variable.As an example, the Kaiser report assists with understanding of the independent variable of age being a factor in why uninsured use a free clinic for their health care needs. It showed that younger working age adults in a certain age range were the group who are most often uninsured, and that this age group is forced to use free health care or have none at all, ultimately having medical conditions worsen, thus costing hospitals and tax payers more in the end. There is currently a staggering estimated $70 billion in uncompensated medical care from 2008 alone by uninsured patients (US Dept. f Health and Human Services, 2011). Therefore it is imperative that those with no medical insurance have access to some form of free or affordable health care in thei r community, with free clinics being an important piece of the equation. Implications The findings of this research are expected to be beneficial to the Shelby County local government, health and human service non-profit agencies and the medical system as the study will be proving assumed information, along with providing ancillary supportive data about the health care needs and gaps to serve uninsured residents of Shelby County, Alabama.In knowing information about what factors contribute to the free clinic usage among the uninsured, the community collaborative can propose modifications, improvements and additions for programming that may assist in lessening the burden, and ultimately solving the problem. While the outcomes from the study may not be exact to national trends, they should be very reflective and allow for reproduction of successful interventions. RecommendationsThe provided research will give evidence on four factors that contribute to the use of free clinics for medi cal treatment by the uninsured population of Shelby County, Alabama thus allowing for a community collaborative to be formed from local government, health care providers, faith based community, caseworkers, immigration and homelessness advocates, university department heads and others. Therefore, it is strongly suggested that this study be performed in order to gather this necessary information to determine if a more detailed needs assessment should be conducted.While there are additional independent variables that may contribute to the usage of a free clinic, only four have been highlighted for this study. Others additional factors should be investigated to identify other challenges that strain the health care system, ultimately contributing to the occurrence of free clinic use. REFERENCES Abdullah, F. et al. , (2009). Analysis of 23 million US hospitalizations: uninsured children have higher all-cause in-hospital mortality. Journal of Public Health, 32 (2), 236–244. doi:10. 093/pubmed/fdp099 Batra, P. , Chertok, J. , Fisher, C. , Manseau, M. , Manuelli, V. , & Spears, J. (2009). The Columbia-Harlem homeless medical partnership: A new model for learning in the service of those in medical need. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 86 (5). doi:10. 1007/s11524-009-9386-z Becker, G. , (2001). Effects of being uninsured on ethnic minorities' management of chronic illness. West Journal of Medicine, 175(1), 19–23. Corso, P. & Fertig, A. , (2011). 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Retrieved from: http://www. whitehouse. ov/blog/2010/05/10/a-long-overdue-change-help-young-adults-get-coverage [pic] [pic] |Appendix B | | |Required Sample Size†  | | | | | | | | | | | | |   |0. 05 |0. 035 |0. 025 |0. 01 |   |0. 05 |0. 035 |0. 25 | |   | |10 |   |10 |10 |10 |10 |   |10 |10 |10 | | | |20 |   |19 |20 |20 |20 |   |19 |20 |20 | | | |30 |   |28 |29 |29 |30 |   |29 |29 |30 | | | |50 |   |44 |47 |48 |50 |   |47 |48 |49 | | | |75 |   |63 |69 |72 |74 |   |67 |71 |73 | | | |100 |   |80 |89 |94 |99 |   |87 |93 |96 | | | |150 |   |108 |126 |137 |148 |   |122 |135 |142 | | | |200 |   |132 |160 |177 |196 |   |154 |174 |186 | | | |250 |   |152 |190 |215 |244 |   |182 |211 |229 | | | |300 |   |169 |217 |251 |291 |   |207 |246 |270 | | | |400 |   |196 |265 |318 |384 |   |250 |309 |348 | | | |500 |   |217 |306 |377 |475 |   |285 |365 |421 | | | |600 |   |234 |340 |432 |565 |   |315 |416 |490 | | | |700 |   |248 |370 |481 |653 |   |341 |462 |554 | | | |800 |   |260 |396 |526 |739 |   |363 |503 |615 | | | |900 |   |269 |419 |568 |823 |   |382 |541 |672 | | | |1,000 |   |278 |440 |606 |906 |   |399 |575 |727 | | | |1,200 |   |291 |474 |674 |1067 |   |427 |636 |827 | | | |1,500 |   |306 |515 |759 |1297 |   |460 |712 |959 | | | |2,000 |   |322 |563 |869 |1655 |   |498 |808 |1141 | | | |2,500 |   |333 |597 |952 |1984 |   |524 |879 |1288 | | | |3,500 |   |346 |641 |1068 |2565 |   |558 |977 |1510 | | | |5,000 |   |357 |678 |1176 |3288 |   |586 |1066 |1734 | | | |7,500 |   |365 |710 |1275 |4211 |   |610 |1147 |1960 | | | |10,000 |   |370 |727 |1332 |4899 |   |622 |1193 |2098 | | | |25,000 |   |378 |760 |1448 |6939 |   |646 |1285 |2399 | | | |50,000 |   |381 | 772 |1491 |8056 |   |655 |1318 |2520 | | | |75,000 |   |382 |776 |1506 |8514 |   |658 |1330 |2563 | | | |100,000 |   |383 |778 |1513 |8762 |   |659 |1336 |2585 | | | |250,000 |   |384 |782 |1527 |9248 |   |662 |1347 |2626 | | | |500,000 |   |384 |783 |1532 |9423 |   |663 |1350 |2640 | | | | Appendix C Health Care Survey Questionnaire Circle your answer: 1. What is your age? a. 19-24 b. 25-34 c. 35-44 d. 45-54 e. 44-64 2. What would you classify your ethnicity? a. Caucasian or white b.African American or black c. Hispanic/Latino d. Asian e. Other________________ 3. What is your employment status? a. Full time employee b. Part time employee c. Self employed d. Unemployed – looking for work e. Unemployed f. Retired 4. Reason for no health care coverage/insurance? a. Employer does not offer b. Don’t work enough hours c. Became unemployed and lost coverage d. Cannot afford 5. What is your highest level of completed education? a. Did not complete High schoo l/did not obtain GED b. High School Diploma / GED c. Technical/Trade school d. Some college e. College degree f. Graduate degree g. Doctoral degree 6. What is your housing status? a.Own home b. Rent home/apartment c. Live with family/friends d. Reside at shelter/transitional housing e. Not housed 7. What language do you speak most often at home? a. English b. Spanish c. Other__________________ 8. Are there children living in your household ages 18 and younger? a. Yes b. No 9. When was the last time you received medical care before today’s visit? a. Within last week b. Within last month c. Within last three months d. Within last six months e. Within last year f. Longer than one year 10. Where did you last receive medical treatment before today’s visit? a. Doctor office b. Hospital ER c. Public health department d. Free Clinic 11.Which best describes the reason you chose the location for your last medical treatment? a. Location b. Hours of operation c. Recommended by fam ily/friend d. Did not know where to go 12. Did you have medical insurance the last time you received medical treatment? a. Yes b. No c. I don’t know 13. How would you rate your satisfaction level of your most recent medical treatment? a. Very satisfied b. Somewhat satisfied c. Somewhat dissatisfied d. Not satisfied 14. How would you describe your health? a. Excellent b. Good c. Fair d. Poor 15. Are you experiencing an ongoing health problem? a. Yes b. No c. I don’t know 16. Have you had a diagnosis for your health problem? a. Yes b. No c. I don’t know 17.Are you taking prescription medications? a. Yes b. No 18. If you are taking prescription medications, is a needed refill the reason for your visit today? a. Yes b. No c. Not applicable 19. How are you able to afford your medications? a. Medication assistance b. Lower cost generics c. Samples d. Self-pay full price e. I cannot afford them 20. Please discuss any other issues you are having where assistance may be needed, so referrals may be offered. 21. Please describe in detail what you hope to receive from your visit today. Appendix D [pic] Shelby County Development Services Profile Appendix E – Example of a Multiple Regression results chart [pic] [pic]