In this section the result of the influence of socio-demographic factors such as gender, age and education, on satisfaction are discussed. Judith A. Hall performed a meta-analysis to examine the relation of patients' socio-demographic characteristics to their satisfaction with medical care. The socio-demographic characteristics included age, sex, socioeconomic status
(three indices), marital status, and family size. He observed that greater satisfaction was significantly associated with greater age and less education, and marginally significantly
associated with being married and having higher social status (scored as a composite variable
emphasizing occupational status). The average magnitudes of relations were very small, with
age being the strongest correlate of satisfaction (mean r = 0.13). For all socio-demographic
variables, the distribution of correlations was significantly heterogeneous, and statistical contrasts revealed the operation of several moderating variables.
Gender and SERVQUAL dimensions
Many literatures have reported that evidence about the effects of gender, ethnicity, and socio- economic status is equivocal due to the small amount of literature available on each (McGee, 1998). The present study used t-test to investigate the scores for males and females in the five health service dimensions. From the results, the dimension in which the male mean value was higher than in female was reliability, at 21.34 and 20.49 for males and females respectively. The highest mean value in female was 21.48 recorded for the assurance dimension. Further, from t-analysis test result the study showed that reliability was the only dimension that had a significant difference in mean values between male and female at F = 14.356 (p=. 0.142). However, the study concluded that there is no significant difference between gender in terms of response.
Age and patient satisfaction
Jaipaul C.K. and Rosenthal G.E. (2003) established that satisfaction exhibits a complex relationship with age, with scores increasing until age 65 to 80 and then declining. The results suggested that age and health status should be taken into account when interpreting patient satisfaction data. Most of the existing patient satisfaction literature suggests that patient satisfaction with health care is positively associated with age (Linn MW, et al., 1982,
Zastowny TR, et al., Tucker JA, Kelly VA (2000). Interestingly, studies have also suggested that the positive association between age and satisfaction extends to areas outside of health care. In one study of 240 welfare clients in 4 U.S. cities, older clients were more likely to be satisfied with welfare services and with treatment in administrative encounters than were younger clients (Goodsell, C.T., 1981).
When age and satisfaction was compared the results concurred with other studies like that done by Jennifer E., et., al. (2009). Satisfaction increased with age and picked at 6.77 out of a possible 7 for the most satisfied then levelled off at 6.68. The reason for this tendency could be because the expectation of the older population is usually more realistic following the many years of interaction with and knowledge of the system as opposed to the younger population whose expectations usually tend to go over and above what is on the ground. They are also more forgiving of the health care inadequacies due to their understanding of the system.
On another level, equity theorists hypothesized that patients perceive their care based on how it measure up to the care of others (Linder-Pelz, 1982). In the interpretation of our study, younger people may not have had the advantage of having a friend or knowledge of what happens beyond their school, or college, or home – characteristic of older patients who may have read, or heard by word of mouth or interacted with other patients with experience. While patient preferences for care and unmet expectations clearly influences how they perceive health care interactions, age may also play a direct role in how these patients are treated. Older patients presents with a lot of challenging conditions which are complex, so perhaps physicians are inundated to take a different approach towards them (Hodes R.J., et. al., 1995). One study concluded that age independently affects attitudes of clinicians (Ntusi N, 2004).
A better understanding of these age-related differences and other factors influencing health care provision could be useful to improving health care service delivery. The under 15 years reported a higher satisfaction than 15-25 years and 26-30 possibly because they were accompanied by older parents or guardians who answered the questionnaire on their behalf.
Patients education, occupation and their corresponding satisfaction
The number of years taken pursuing education reflects the level of education. Since the introduction of 8-4-4 system of education in Kenya, a candidate is expected to take up to 8 years to qualify for the primary education certificate (KCPE), 12 years for Kenya Certificate of Secondary Education (KCSE) and 16 years for the first university degree.
Some studies have suggested that education of patients, if they are primary or higher levels compared to education of patients less than primary has insignificant effect on satisfaction with care (Tayyaba B., et. al., 2011). Yet another study found that patient socio-demographic characteristics such as years of education and income act as influential factors to patient satisfaction or dissatisfaction with the quality of care (Laila A. and Mohammad J.U., 2011).
A regression analysis was performed to establish whether education accounted for variations and by how much. From the results, the study established that education accounts for no variation in satisfaction, meaning that 100% of the variation in satisfaction cannot be explained by education alone. Therefore there must be other factors or variables that have influence on satisfaction also.
To find out how socio-demographic factors such as occupation influenced satisfaction, a regression analysis was performed. The study established that occupation accounted for 0.1 % variation in satisfaction, meaning that 99.9 % of the variation in satisfaction couldn‟t be
explained by occupation alone. Therefore there must be other factors or variables that had influence on satisfaction also.
Similarly, the ANOVA table puts the F value at 0.071 which is not significant at p˂0.001 because this is greater than 0.001, i.e. 0.791. This implies that there is more than 79.1 % chance that F-ratio of 0.071 would happen if there was a positive influence of occupation on satisfaction. We conclude that the regression model results in significantly worse prediction of influence of occupation on satisfaction than if we used the mean value of occupation influence on satisfaction.
In conclusion, Null Hypothesis could not be accepted –that socio-demographic factors (education and occupation had a positive influence on patients satisfaction.
Biomarkers influence on patients satisfaction.
The patients biomarkers had been envisioned to include Cd4++, the Cd8++ and the haemoglobin levels. The study established that the above first two biomarkers rarely gets performed within the health facilities due to lack of facilities and related manpower. This left Cd4++ cell counts as the only biomarker studied. Many protocols and treatment studies including that issued by WHO puts Cd4++ cell count at below 200 for a patients to commence the ART intake. The results showed the range of Cd4++ cell counts for the patients under the study. From the diagram it was observed that 18.26% of the responents had a Cd4++ cell count of below 200. The Cd4++ cell count group with the highest number of respondent was 200 to 400 group with 31.74% of the respondents. The cd4++ counts was seen to be relatively high in most of the patients, an indication that most of them lead a normal life without ART administration, hence without the accompanying dreaded side effects.
To find out more on the effect of Cd4++ cell count on satisfaction cross tabulation was done between Cd4++ cell count and one of the physical activities. From the results it was observed that Cd4++ cell count below 200 row had the highest percentage within row that indicated that they were limited a lot where the count is 10 in comparison to expected count of 8 this being 15.6% within the row. This could be attributed to the fact that at this level of Cd4++ cell count a person is prone to diseases that weaken them physically. The Cd4++ group of 200 to 400 had the highest count of 14 respondents that indicated that they were limited a lot which is equal to the expected count and represents 12.5% within the row. Similarly, it was also established that the the Cd4++ group of 800 and above had the lowest number of respondents who were limited alot for the activities and also the highest percentage within the row for those who indicated that they were not limited at all in the activity under consideration. This can be attributed to the fact that at this level of Cd4++ cell count most people will be physically strong. From the table it was clear that there was no significant difference between the expected counts and the actual counts or simply stated that there is no significant relationship between the Cd4++ count for the patient and their ability to perform the physical activities. One explanation to this observation is that most of the respondents had Cd4++ cell counts within a range that made them less prone to diseases and thus were not affecting their ability to perform physical activities on count of the Cd4++cell count levels. The other explanation could be that some of the respondents may have been affected in their physical ability because they were affected emotionally and this has affected their physical strength irrespective of their Cd4++ levels. The lack of a strong relationship between the variable is clearly seen from the chi-square test result in table 4.20. From the table it was clear that there is no significant relations between the variables at the Pearson chi-square value of 7.788 at p = 0.454.The above results corroborated past work that asserts that distinguishing between the experience of sickness or experience of health service
treatment or other factors as causes of dissatisfaction has proven difficult (Hall and Milburn, 1998; Cleary et al, 1992).