Perception of occupational health and safety in the manufacturing sector: a qualitative evaluation
Bibliographic record
Abstract
Objectives. Earlier work found gaps with respect to legislative compliance and disparities in perceptions, attitudes and beliefs towards occupational health and safety in the Ontario manufacturing sector. The current follow-up study was undertaken to gain a more thorough understanding of the cause of these gaps and differences in perspectives. Methods. Focus group discussions were held with workers and managers separately. Key questions related to health and safety in general, health and safety training, and health and safety communication were asked of each focus group. The discussions were qualitatively analysed. Results. Overall, 12 worker focus groups (n = 76) and seven manager focus groups (n = 38) were conducted. Individuals who felt safe in their workplace indicated that it was a supportive environment, and that health and safety was a priority. Health and safety training was considered important but improvements in engagement and frequency were suggested. Conclusions. Health and safety communication might be hindered by technical terms and language barriers. Delivering this communication in multiple ways as well as the tone of communication should be taken into consideration. Overall, safety culture was lacking and manufacturing workplaces should be mindful of the gaps identified to improve health and safety performance.
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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 | 0.034 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".