Successful Strategies for Occupational Health and Safety in Small and Medium Enterprises: Insights for a Sustainable Return to Work
Bibliographic record
Abstract
PURPOSE: The objectives of this paper were to summarize successful strategies in occupational health and safety (OHS) management in small and medium-sized enterprises (SMEs) and to explore their potential applicability for disability management (DM) and return-to-work (RTW) after work-related injuries. METHODS: A scoping review was conducted, using a consensus-based iterative approach, and a consultation with stakeholders. Twelve databases were searched in collaboration with a specialized librarian, using keywords and combinations of terms. The reviewers identified pertinent articles, selected those which corresponded to the inclusion criteria, extracted data, and analyzed information using qualitative content analyses. A synthesis was presented to the stakeholders, and their comments on overall applicability of these strategies in the Quebec context of DM and RTW were detailed. RESULTS: A total of 638 references were retrieved from all sources, resulting in 37 scientific articles being analyzed. Four main strategies for improving OHS management in SMEs were identified: dissemination and exchange of information; transmission and acquisition of knowledge, skills, and abilities; using a participatory and collaborative approach; and considering the organizational context. Even if DM and sustainable RTW were sometimes mentioned by authors as important for OHS management in SMEs, specific strategies and implementation elements were not actually described by authors. However, different resources, structures, and activities, associated with one or more of the OHS management strategies described, through their different interactions between the various stakeholders, seem having the potential to act also in sustainable RTW. CONCLUSION: This review has provided an overview of strategies deployed to improve OSH in SME. The results invite stakeholders to a deep reflection on the potential application of such strategies to encompass sustainable RTW in SMEs.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".