A Systematic Search and Review of Questionnaires Measuring Individual psychosocial Factors Predicting Return to Work After Musculoskeletal and Common Mental Disorders
Bibliographic record
Abstract
Purpose Individual psychosocial factors are crucial in the return to work (RTW) process of workers with musculoskeletal disorders (MSDs) and common mental disorders (CMDs). However, the quality and validity of the questionnaires used to measure these factors have rarely been investigated. The present systematic search and literature review aims at identifying, categorizing, and evaluating the questionnaires (measurement tools) used to measure individual psychosocial factors related to the perception of the personal condition and motivation to RTW that are predictive of successful RTW among workers with MSDs or CMDs. Methods Through a systematic search on PubMed, Web of Science, and PsycINFO library databases and grey literature, we identified the individual psychosocial factors predictive of successful RTW among these workers. Then, we retrieved the questionnaires used to measure these factors. Finally, we searched for articles validating these questionnaires to describe them exhaustively from a psychometric and practical point of view. Results: The review included 76 studies from an initial pool of 2263 articles. Three common significant predictors of RTW after MSDs and CMDs emerged (i.e., RTW expectations, RTW self-efficacy, and work ability), two significant predictors of RTW after MSDs only (i.e., work involvement and the self-perceived connection between health and job), and two significant predictors of RTW after CMDs only (i.e., optimism and pessimism). We analyzed 30 questionnaires, including eight multiple-item scales and 22 single-item measures. Based on their psychometric and practical properties, we evaluated one of the eight multiple-item scales as questionable and five as excellent. Conclusions: With some exceptions (i.e., self-efficacy), the tools used to measure individual psychosocial factors show moderate to considerable room for improvement.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".