The psychosocial work environment and musculoskeletal disorders: Design of a comprehensive interviewer‐administered questionnaire
Bibliographic record
Abstract
BACKGROUND: Psychosocial working conditions are likely to contribute to work-related musculoskeletal disorders (WRMSDs), but a lack of standardized measurement tools reflects both the theoretical and methodological limitations of current research. METHODS: An interdisciplinary team including biomedical, behavioral, and social science researchers used an iterative process to adapt existing instruments for an interviewer-administered questionnaire assessing psychosocial workplace exposure related to musculoskeletal disorders. RESULTS: The resulting questionnaire included measures of psychosocial workplace factors based on two theoretical models (the demand-control-support and the effort-reward imbalance models), supplemented by the additional constructs of "emotional demands," and "experiences of discrimination." Other psychosocial and physical measures selected for questionnaire inclusion address physical workload, sociodemographic and anthropometric characteristics, social relations and life events, health behaviors, and physical and psychological health. CONCLUSION: Using an interdisciplinary approach facilitated the development of a comprehensive questionnaire inclusive of key measures of psychosocial factors that may play a role in the complex mechanisms leading to WRMSDs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".