8282319 Supporting prevention through measurement: dual-sample validation of the questionnaire on psychosocial risks, well-being, and health at work (QRBEST) in québec
Bibliographic record
Abstract
<h3>Objective</h3> There is a growing need for accessible and scientifically robust tools to measure psychosocial risks at work (PRW), particularly considering evolving occupational health legislations and the diverse realities of workplaces, including those of small and medium-sized enterprises (SME). This study aims to evaluate the stability and psychometric properties of the French version of the Questionnaire on Psychosocial Risks, Well-being, and Health at Work (QRBEST) across two complementary samples, supporting its use as a valid tool to assess PRW and their associated outcomes. <h3>Methods</h3> A cross-sectional study was conducted with 3000 French-speaking workers from a large, demographically diverse populational panel that used quotas to reflect the sociodemographic characteristics of Québec’s working population as well as a second sample of 704 employees from eight Québec SMEs. Both samples completed the QRBEST, assessing 22 PRWs and 12 indicators of well-being, health, and productivity. Data collection took place from October-November 2024 for the panel and August-November 2024 for the SMEs (average participation rate: 72%). Psychometric analyses included internal consistency (Cronbach’s alpha), test-retest reliability (in panel only), item-total correlations, item response theory, and both exploratory and confirmatory factor analyses, conducted separately in both samples. <h3>Results</h3> In both samples, the QRBEST demonstrated strong psychometric properties. All multi-item scales showed good to excellent internal consistency (α>.75). Exploratory and confirmatory factor analyses supported the expected factor structures and showed excellent model fit (SRMR<0.08; GFI≈1), indicating that the QRBEST performs reliably across different workplace contexts. <h3>Conclusion</h3> The QRBEST is a scientifically robust instrument for assessing PRW and their associated outcomes. Its strong performance across both general and SME samples supports its practical use in diverse workplaces, making it suitable for a wide range of occupational settings and helping bridge the gap between risk identification and preventive action. <h3>Funding</h3> Fonds institutionnel de recherche de l’Université du Québec à Rimouski.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".