Innovation in the occupational health physician profession requires the development of a work collective to improve the efficiency of MSD prevention
Bibliographic record
Abstract
Given the ageing population of occupational health physicians and the deteriorating situation of employee health, reforms targeting the multi-disciplinary nature of occupational health are currently being drawn up. These are of great concern to doctors in terms of the future of occupational health, notably with regard to changing medical practices. The objective of this study is to explore the actual practices of occupational health physicians within the framework of MSD prevention in France. By analysing the activity of occupational health physicians, we could gain a better understanding of the coordination between those involved in OHS with the ultimate goal being to improve prevention. Based on an analysis of peer activity, this method made it possible to push beyond pre-constructed discourse. According to activity theories, it is through others that the history and controversies of a profession can be grasped and skills developed. The results produced by these collective discussions on activity analysis contributed to establish a collective point of view about the important aspects of their profession that need defending and the variations in professional genre in relation to the current reforms, notably.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".