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. ^

Simulation study of joint transition and Weibull survival model with shared parameters

This study investigates a theoretical model where a longitudinal process, that is a stationary Markov-Chain, and a Weibull survival process share a bivariate random effect. Furthermore, a Quality-of-Life adjusted survival is calculated as the weighted sum of survival time. Theoretical values of population mean adjusted survival of the described model are computed numerically. The parameters of the bivariate random effect do significantly affect theoretical values of population mean. Maximum-Likelihood and Bayesian methods are applied on simulated data to estimate the model parameters. Based on the parameter estimates, predicated population mean adjusted survival can then be calculated numerically and compared with the theoretical values. Bayesian method and Maximum-Likelihood method provide parameter estimations and population mean prediction with comparable accuracy; however Bayesian method suffers from poor convergence due to autocorrelation and inter-variable correlation. ^

Association between cardiorespiratory fitness and Alzheimer's mortality: A prospective cohort study

Context. Alzheimer’s disease is a major source of morbidity and mortality in aging societies. Preventive measures, such as increasing cardiorespiratory fitness, to reduce the risk of Alzheimer’s disease mortality have not been sufficiently examined.^ Objective. To examine the association between levels of cardiorespiratory fitness and Alzheimer’s disease mortality.^ Design, Setting, and Patients. A prospective cohort study of 53,911 men and 18,876 women (mean age, 51.4 [SD, 10.0] years; range 20-88) enrolled in the Cooper Center Longitudinal Study who completed a baseline health examination during 1970-2006. The primary exposure, cardiorespiratory fitness, was assessed via a maximal exercise test. Fitness was categorized according to age- and sex-specific tertiles based on the participants’ distribution of maximal treadmill exercise test duration, in metabolic equivalent tasks (METs). The main outcome measure was Alzheimer’s disease mortality, defined as the underlying or contributing cause of death using the National Death Index and death certificates through December 31, 2006.^ Results. There were 175 Alzheimer’s disease deaths during a mean follow up of 37 years and 1,309,170 person-years of exposure. Women in the high fitness category had a 70% reduction in risk of Alzheimer’s mortality compared to women in the low fitness category (HR=0.3; 95% CI, 0.1-0.8; P=.02), while adjusting for potential confounders. Similarly, women in the moderate fitness category had a 70% reduction in risk for AD mortality compared to women in the low fit category (HR=0.3; 95% CI, 0.1-0.7; P=.005). Among men, the relationship between fitness level and AD mortality risk was examined but none were of statistical significance. The adjusted comparison of men in the high fitness category to low fit men yielded an HR of 0.9 (95% CI, 0.6-1.5; P=.79), while moderately fit men compared to low fit men yielded an HR of 1.3 (95% CI, 0.9-1.9; P=.21).^ Conclusions. Higher levels of cardiorespiratory fitness were associated with decreased risk of AD mortality, in women. No statistically significant association was found among men. Physical fitness may be an important protective factor against Alzheimer’s disease death in women, further supporting its clinical and public health values.^

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. ^