Occupational health and safety hazards encountered by Ontario Public Health Inspectors
Bibliographic record
Abstract
Public health inspectors (PHIs) are exposed to many occupational health and safety issues during their daily tasks. However, to our knowledge, no study has investigated the job-specific health and safety hazards among working PHIs. Our objective was to determine the type and extent of health and safety hazards faced by PHIs working for Ontario health units as well as their perception of risk with respect to these hazards. In early 2018, an invitation to a web-based survey was sent to all members of the Canadian Institute of Public Health Inspectors Ontario Branch. One-hundred and thirty-four respondents met the inclusion criteria and were included in the study. Results showed PHIs reported safety hazards (e.g., slips or falls), working alone, and chemical hazards as the top three types of hazards. Inspections of food and (or) nonfood premises were the duties most associated with encountering all types of hazards. In addition, a majority of respondents reported being somewhat concerned about their exposure to all types of hazards. This study provides novel information on the occupational health and safety risks as reported by Ontario PHIs. Further in-depth research is needed to investigate the specific hazards and concerns among PHIs as well as the level of prevention and monitoring within health units.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".