P-602 CHALLENGES AND LEVERS IN DISABILITY MANAGEMENT AND SUSTAINABLE RETURN TO WORK IN SMALL AND MEDIUM ENTERPRISES
Bibliographic record
Abstract
Abstract Introduction Recommended practices on disability management and return to work (RTW), mainly studied in large companies, cannot necessarily be applied in small and medium enterprises (SME). This presentation aims to elucidate some challenges and levers associated with achieving sustainable RTW in SME. Methods A qualitative research design, including semi-structured interviews with 8 SME managers in the manufacturing sector, and 2 focus groups with 16 external stakeholders (health professionals, ergonomists, mutual prevention agents and insurers) were conducted. Content analyses of the verbatim were performed, using a mix-coding method and an iterative and consensus-driven approach reached (3 inter-raters). Results Despite some interest in practices oriented towards sustainable RTW, participants indicated a lack of resources and some deficiencies in the structural frameworks hindering their effective implementation in SME. Identified challenges were the expertise and experience of those responsible for managing workplace injuries, and interfaces that complicate efficient communication between medical professionals, insurers and mutual prevention agents. These challenges appear most pronounced in smaller companies, unable to compensate for this shortfall through the close internal relationship between players. Explicit, easy-to-apply procedures, along with the support of professionals, are the main levers identified to promote sustainable RTW. Discussion Streamlined, collaborative and comprehensive practices emerge as essential catalysts in decision-making concerning the management of injured employees within SMEs. The emphasis on collaboration proves particularly important in such environments. Conclusion Innovative practices have the capacity to provide enhanced support for achieving sustainable RTW outcomes among a population of workers who frequently remain on the periphery of occupational health initiatives within smaller workplaces.
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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 | 0.002 | 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".