Predicting occupational strain and job satisfaction: the role of stress, coping, personality, and affectivity variables

Four studies employed path analysis to examine how measures of occupational stressors, coping resources, and negative affectivity (NA) and positive affectivity (PA) interact to predict occupational strain. The Occupational Stress Inventory (Osipow & Spokane, 1987) was used to measure stress, strain, and coping. The Positive and Negative Affectivity Schedule (Watson, Clark, & Tellegen, 1988) was used for the affectivity variables. The hypothesised model showed NA and PA as background dispositional variables that influenced relations among stress, strain, and coping while still allowing stress and coping to have a direct influence on strain. Goodness of fit indices were acceptable with the model predicting 15 per cent of the variance in stress, 24 per cent of coping, and 70 per cent of strain. Study 2 replicated these findings. Study 3 added a positive outcome variable, job satisfaction (JSI: Brayfield & Rothe, 1951) to the model. The expanded model again fit the data well. A fourth study added a global measure of personality (NEO-FFI: Costa & McCrae, 1991) to the model tested in Study 3. Results indicated that personality measures did not add anything to the prediction of job satisfaction and strain in a model that already included measures of stressors, coping resources, NA and PA. The series of four studies yielded a reliable structural model that highlights the influence of organizational and dispositional variables on occupational strain and job satisfaction

Criteria for the use of omics-based predictors in clinical trials: Explanation and elaboration

High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well. © 2013 McShane et al.; licensee BioMed Central Ltd

WILLIAMS et al. UPDATES AND DEVELOPMENTS IN ONCOLOGY

tumour response to cancer therapy based on the oracle of genetics P.D. Williams PhD, * J.K. Lee PhD, * and D. Theodorescu MD PhD † Cells are complex systems that regulate a multitude of biologic pathways involving a diverse array of molecules. Cancer can develop when these pathways become deregulated as a result of mutations in the genes coding for these proteins or of epigenetic changes that affect gene expression, or both 1,2. The diversity and interconnectedness of these pathways and their molecular components implies that a variety of mutations may lead to tumorigenic cellular deregulation 3–6. This variety, combined with the requirement to overcome multiple anticancer defence mechanisms 7, contributes to the heterogeneous nature of cancer. Consequently, tumours with similar histology may vary in their underlying molecular circuitry 8–10, with resultant differences in biologic behaviour, manifested in proliferation rate, invasiveness, metastatic potential, and unfortunately, response to cytotoxic therapy. Thus, cancer can be thought of as a family of related tumour subtypes, highlighting the need for individualized prediction both of disease progression and of treatment response, based on the molecular characteristics of the tumour