Fréquences d’exposition aux principaux facteurs de risque biomécaniques d’usure professionnelle chez les femmes et les hommes dans la cohorte CONSTANCES
Bibliographic record
Abstract
Introduction: As part of the 2023 pension reform in France, a fund for the prevention of occupational wear and tear (fonds pour la prévention de l'usure professionnelle, FIPU) was set up to prevent premature aging and musculoskeletal disorders.In accordance with French law, the FIPU takes into consideration exposure to three biomechanical criteria: manual handling of loads, painful positions at work, and mechanical vibrations.The aim of our study was to identify the occupational categories most exposed to the three FIPU criteria, as well as to another biomechanical risk factor for musculoskeletal disorders: repetitive work.Methods: The analyses are based on cross-sectional data from the French CONSTANCES cohort (Consultants des centres d'examens de santé).Exposure to the three FIPU criteria and to repetitive work were described according to occupational categories (based on the French nomenaculture for occupational groups [Familles professionnelles, FAPs]).Results: Among men, the FAPs most exposed to at least one of the three FIPU criteria were skilled construction workers (structural and finishing work: 95.8% and 88.1%) and metal workers (86.0%).Among women, the FAPs most impacted were home help personnel and cleaners (80.3%), assistant nurses (79.2%), and childcare assistants (77.1%).Adding the repetitive work factor increased the proportion of exposed workers, particularly among women, and highlighted new occupations, not obtained with the FIPU criteria alone.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".