Facilitators and barriers to occupational health and safety in small and medium-sized enterprises: a descriptive exploratory study in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: The purpose of this particular study was to test a newly created instrument in describing the facilitators and barriers to occupational health and safety in small and medium-sized enterprises (SMEs) in Ontario, Canada. METHODS: A cross-sectional design was used to identify the occupational health and safety culture of SMEs in public and private sectors in Ontario. RESULTS: A total of 153 questionnaires were completed. The majority of respondents were female (84%) with a mean age of 49.8 years (SD 10.6). Seventy-four percent were supervisors. Seventy percent of respondents were from the private sector while 30% derived from the public sector including healthcare, community services, and non-profit organizations. Further, conducting regular external safety inspections of the workplace was found to be statistically associated with a safe work environment 2.88 95% CI [1.57, 5.27]. CONCLUSIONS: Strategies and training opportunities that focus on how to adapt occupational health and safety legislation to the nature and diversity of SMEs are recommended. Furthermore, employers may use such information to improve safety in their SMEs, while researchers can hopefully use such evidence to develop interventions that are applicable to meeting the occupational health and safety needs of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".