Bibliographic record
Abstract
Organizations are facing ever-stiffer competition in the current globalized economy, and employees are consequently being exposed to increasingly adverse psychosocial work factors. Psychosocial work factors, also called psychosocial stressors, refer to all organizational factors and interpersonal relationships in the workplace that may affect workers' health. Two well-defined and internationally recognized theoretical models are used to assess these factors: the Karasek demand-latitude-support model and the Siegrist effort-reward imbalance model. The Karasek and Siegrist models reflect specific components of the work environment for which there is empirical evidence of a deleterious effect on health. Preventive interventions targeting these factors are conducted in workplaces. However, few studies have rigorously documented these interventions and their effectiveness in reducing adverse work factors and improving health outcomes. Most previous intervention studies were limited by (1) a short follow-up that may not have provided sufficient time for effects to appear, (2) small sample sizes (N ≤ 100) that limited the statistical power and the possibility of detecting results and (3) rare assessment of the Siegrist model. The current paper presents the overall design and the main results of an intervention study on psychosocial work factors and related mental health and musculoskeletal outcomes. The study integrated (1) a development phase that aimed at identifying the changes needed to reduce psychosocial factors in the target population and the best ways to bring about these changes, (2) an implementation phase that systematically documented how the intervention was carried out and (3) an effectiveness phase that evaluated whether the intervention was successful in reducing adverse psychosocial work factors and health problems. In addition, the study used repeated measurements of psychosocial work factors and health indicators at baseline and six and 30 months post-intervention to assess short- and medium-term effects of the intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".