Detecting Gender Discrimination in University Salaries: A Case Study

To assess sex discrimination in university salary allocation accurately, one must determine whether gender explains the salary difference in and of itself, or exerts its influence through other variables, such as rank and departmental affiliation, that themselves affect salary and may correlate with gender. Using members of the Faculty of Social Science (N = 133) of a large Canadian university as a case sample, we assessed gender discrimination in promotion and gender differences in departmental affiliation as related to salary before including these two variables in statistical analyses predicting salary. No evidence was found for discrimination in promotion and women were not morie under-represented in the higher-salaried departments. Several regression models recommended in the literature for assessing gender discrimination in salaries were conducted and yielded convergent findings : male and female faculty similar on salary-relevant variables were equivalently paid. While these results should be reassuring, they would not go very far toward resolving salary discrimination disputes in the university studied or in most other academic institutions. The difficulties of applying the results of statistical analyses within a politically-charged arena are discussed.Afin d'évaluer avec précision le degré de discrimination sexuelle dans l'allocation des salaires universitaires, on doit déterminer si le sexe en soi explique la différence salariale ou s'il exerce son influence par l'intermédiaire d'autres variables, telles le rang et les affiliations départementales, qui influencent elles-mêmes les salaires et qui pourraient être en corrélation avec le sexe. Prenant comme échantillon représentatif le corps professoral de la faculté des sciences sociales (TV = 133) d'une grande université canadienne, on a évalué la discrimination sexuelle dans l'avancement et les différences sexuelles dans les affiliations départementales se rapportant aux salaires avant d'inclure ces deux variables dans les analyses statistiques pouvant predire les salaires. On n'apporte aucun appui à l'existence de discrimination dans /'avancement et le nombre de femmes affiliées aux départements dont les salaires sont plus élevés n'est pas inférieur. Plusieurs modèles de régression recommandés dans la documentation concernant l'évaluation de la discrimination sexuelle ont été effectués et ont produit des résultats convergents: qu'il s'agisse d'hommes ou de femmes, les professeurs qui correspondaient de façon semblable aux variables se rapportant aux salaires étaient rémunérés de façon égale. Tandis que ces résultats devaient être rassurants, on observe qu'ils n'aideront pas beaucoup à résoudre les disputes sur la discrimination salariale dans la plupart des institutions académiques y compris la nôtre. Sont abordées les difficultés d'appliquer les résultats d'analyses statistiques dans un milieu très politisé

Deception in Research on the Placebo Effect

A common feature of research investigating the placebo effect is deception of research participants about the nature of the research. Miller and colleagues examine the ethical issues surrounding such deception

Explaining the causal links between illness management and symptom reduction: Development of an evidence-based patient education strategy

Objective: To determine whether explaining the causal links between illness management and symptom reduction would help younger and older adults learn and apply health information. Method: Ninety younger and 51 older adults read about a fictitious disease with or without explanations about the cause-and-effects (causal information) of illness management. A knowledge test (applied vs. factual items) was administered immediately and 1-week following the presentation of health booklets. Reading comprehension, working memory and health literacy were assessed as covariate variables. Results: Younger adults outperformed older individuals on the applied and factual items at both time points. After controlling for covariates, causal information facilitated the comprehension and application of health information for younger but not older adults. Reading comprehension was the best predictor of test performance in the older sample. Conclusions: Providing an explanation of why illness management is effective for reducing symptomatology can help improve knowledge and application of health information for younger individuals. For older adults, lowering the verbal demands of patient education materials may be a better way to help them learn new health information. Practice implications: Use of causal information as a teaching strategy in patient education may enhance individuals\u27 ability to learn about and implement self-care strategies

Can causal explanations about endothelial pathophysiology benefit patient education? A cluster randomized controlled trial in cardiac rehabilitation

Objective: To examine whether explaining causal links among endothelial pathophysiology, cardiac risk factors, symptoms and health behaviors (termed causal information) enhances patients’ depth of knowledge about cardiovascular disease self-management and their perceptions of the cardiac rehabilitation and secondary prevention (CRSP) program. Methods: Newly referred CRSP patients (N = 94) were cluster randomized to usual care (control; UC) or usual care with causal information (intervention; UC + CI). Depth of knowledge (factual vs. deep) was measured with an adapted cognitive-reasoning task. Patients’ cardiovascular knowledge and beliefs about the efficacy of a CRSP program were assessed. Results: After controlling for education level, patients in UC + CI demonstrated deeper knowledge about cardiovascular management than did those in UC. The UC + CI group showed higher factual knowledge than their counterparts after covarying education, occupation status and BMI. The UC + CI group also rated the CRSP program as more credible than those in UC, after controlling for age. Deep knowledge mediated the relationship between group conditions and perceived credibility of CRSP. Conclusion: Causal information can enhance the depth of patients’ understanding of cardiovascular disease management and perceived treatment credibility of the CRSP program. Practice implications: Explaining causal links may help improve patient education delivery and enhance patient engagement in CRSP