0010 The importance of conducting regular safety inspections in small and medium size enterprises0010 The importance of conducting regular safety inspections in small and medium size enterprises
Bibliographic record
Abstract
Objectives A considerable effort has been made to examine the health and safety of employees in large-sized enterprises. However, there has not been much attention given to the organisation of work, occupational health and safety, and work disability prevention in small and medium enterprises (SME). The purpose of our study is to examine facilitators and barriers to occupational health and safety among SME in Ontario. Method A cross-sectional design was used to examine the occupational health and safety culture of small and medium sized enterprises from public and private sectors in Ontario. A convenience sample of employees from all position titles in Ontario organisations that ranged from 5 to 100 full-time equivalent employees were invited via email to participate in the survey. Results A total of a 153 questionnaires were returned. Most of the respondents were female (84.2%) with a mean age of 49.8 years (SD = 10.6). Multivariable logistic regression modelling revealed the odds of a safe work environment for SME who conducted regular safety inspections were estimated to be 2.88 (95% CI, 1.57–5.27) greater than the odds of a safe work environment for SME who did not conduct regular safety inspections. Conclusions This study profiled the work and safety among small and medium enterprises in Ontario. Moreover, better implementation and training strategies that focus on adapting occupational health and safety legislation to the nature and diversity of SMEs is warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".