Early Occupational Health and Safety Interventions for Small Businesses: An Environmental Scan
Bibliographic record
Abstract
BACKGROUND: Small businesses (SB) constitute a significant proportion of businesses in all major industrial sectors and pose challenges to occupational health and safety (OHS) authorities. They contribute disproportionately to the total burden of work-related injuries, illnesses, and fatalities. Reaching SB early in their life cycle to support OHS could decrease injuries and related burden. Our objective was to describe the nature of early OHS interventions for SB. METHODS: We conducted an environmental scan (ES) of OHS interventions that could be implemented early in SB. We searched for documents from peer-reviewed literature, non-peer-reviewed literature, and websites. Findings from the documents were synthesized using a framework of intervention types from Michie et al. We also conducted interviews with 11 key informants who had experience with OHS in SB and, using a qualitative thematic analysis, produced a narrative summary. We synthesized the document review and interview findings. RESULTS: We found 20 relevant documents from all sources describing 24 OHS interventions for SB that could be applied early. The most prevalent SB interventions were education (increasing knowledge), enablement (through consulting and tools), training (imparting skills), and persuasion (through assessment, feedback, and planning). The interview data revealed similar types of interventions, but informants often noted an explicit focus on reaching businesses early. CONCLUSIONS: Our findings reveal that there are few published OHS interventions explicitly focused on application early in the life cycle of SB. However, there were 24 interventions identified that could be applied early, most often focusing on education, enablement, training, and persuasion.
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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 | 0.040 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".