THE EFFECT OF PERSON-ENVIRONMENT FIT ON THE PSYCHOLOGICAL WELL-BEING OF PSYCHIATRIC AIDES WORKING IN A STATE HOSPITAL (COPING RESOURCES, SOCIAL SUPPORT, OCCUPATIONAL STRESS, LOCUS-OF-CONTROL)

This study addressed two purposes: (1) to determine the effect of person-environment fit on the psychological well-being of psychiatric aides and (2) to determine what role the coping resources of social support and control have on the above relationship. Two hundred and ten psychiatric aides working in a state hospital in Texas responded to a questionnaire pertaining to these issues.^ Person-environment fit, as a measure of occupational stress, was assessed through a modified version of the Work Environment Scale (WES). The WES subscales used in this study were: involvement, autonomy, job pressure, job clarity, and physical comfort. Psychological well-being was measured with the General Well-Being Schedule which was developed by the National Center for Health Statistics. Co-worker and supervisor support were measured through the WES and finally, control was assessed through Rotter's Locus of Control Scale.^ The results of this study were as follows: (1) all person-environment (p-e) dimensions appeared to have linear relationships with psychological well-being; (2) the p-e fit - well-being relationship did not appear to be confounded by demographic factors; (3) all p-e fit dimensions were significantly related to well-being except for autonomy; (4) p-e fit was more strongly related to well-being than the environmental measure alone; (5) supervisor support and non-work related support were found to have additive effects on the relationship between p-e fit and well-being, however no interaction or buffering effects were observed; (6) locus of control was found to have additive effects in the prediction of well-being and showed interactive effects with work pressure, involvement and physical comfort; and (7) the testing of the overall study model which included many of the components mentioned above yielded an R('2) = .27.^ Implications of these findings are discussed, future research suggested and applications proposed. ^

Engaging stakeholders in community-level obesity prevention programming: Evaluation of the Fit Family Challenge

Introduction: Both a systems approach to change and a focus on multi-sector interventions ensures obesity prevention programming within the community is equitable, sustainable, and cost-effective. An authentic community engagement approach is required to implement interventions guided by best-evidence research and practice. Although there are examples illustrating the benefits of community engagement, there is no standardized method to implement it. The San Antonio Sports Foundation (SA Sports), a non-profit community-based organization, implements a variety of free events and programs promoting active life styles. One such program is the Fit Family Challenge which is a summer-long program implemented at the school level targeted at families. ^ Aims: This thesis was a culmination of the experience from the student collaborating with SA Sports as part of a practicum opportunity. Using secondary data collected by the Fit Family Challenge during the 2011 year, the goals of this thesis were: to assess individual changes; evaluate short-term impact; and describe the community engagement process. ^ Methods: SA Sports collected quantitative and qualitative data during the implementation and evaluation of the FFC program. SA Sports allowed the used of de-identified data to be analyzed to study the aims of this thesis. ^ Results: The program was able to provide families with the knowledge, information, and opportunity to exercise as a family and cook healthier meals. School district coordinators were generally satisfied and illustrated the benefits of a community partnership. An authentic community engagement was present highlighting the importance of communication, collaboration and the sustainability of such partnerships in the community. ^ Conclusion: The success of an obesity program should focus on triggers that initiate behavioral change rather than physiological changes. The evaluation was guided by a community engagement approach, which illustrated the development of new partnerships and the strengthening of other collaborations. Ultimately, the engagement approach empowered the community to identify their own problems and build collaboration, rather than tackling obesity prevention alone. ^

Evaluation of the Hosmer -Lemeshow global goodness -of-fit statistic for mixed variable logistic regression models

The performance of the Hosmer-Lemeshow global goodness-of-fit statistic for logistic regression models was explored in a wide variety of conditions not previously fully investigated. Computer simulations, each consisting of 500 regression models, were run to assess the statistic in 23 different situations. The items which varied among the situations included the number of observations used in each regression, the number of covariates, the degree of dependence among the covariates, the combinations of continuous and discrete variables, and the generation of the values of the dependent variable for model fit or lack of fit.^ The study found that the $\rm\ C$g* statistic was adequate in tests of significance for most situations. However, when testing data which deviate from a logistic model, the statistic has low power to detect such deviation. Although grouping of the estimated probabilities into quantiles from 8 to 30 was studied, the deciles of risk approach was generally sufficient. Subdividing the estimated probabilities into more than 10 quantiles when there are many covariates in the model is not necessary, despite theoretical reasons which suggest otherwise. Because it does not follow a X$\sp2$ distribution, the statistic is not recommended for use in models containing only categorical variables with a limited number of covariate patterns.^ The statistic performed adequately when there were at least 10 observations per quantile. Large numbers of observations per quantile did not lead to incorrect conclusions that the model did not fit the data when it actually did. However, the statistic failed to detect lack of fit when it existed and should be supplemented with further tests for the influence of individual observations. Careful examination of the parameter estimates is also essential since the statistic did not perform as desired when there was moderate to severe collinearity among covariates.^ Two methods studied for handling tied values of the estimated probabilities made only a slight difference in conclusions about model fit. Neither method split observations with identical probabilities into different quantiles. Approaches which create equal size groups by separating ties should be avoided. ^