Landscape of Artificial Intelligence Use for Occupational Health and Safety Practice in Two Canadian Provinces
Bibliographic record
Abstract
BACKGROUND: Artificial intelligence (AI) can modernize occupational health and safety (OHS) practice and provide solutions to the most complex health and safety challenges. Empirical data on firm-level AI utilization in OHS practice remain limited. The objective of this study was to examine AI use for OHS and firm-level descriptive and OHS characteristics associated with AI use. METHODS: A total of 810 OHS professionals in British Columbia and Ontario, Canada were surveyed in the summer of 2024. Surveys asked about firm-level AI use for OHS and items asked about descriptive and OHS characteristics. Participants were also asked about perceived AI concerns and OHS impact. A multivariate logistic regression model was fitted to examine factors associated with firm-level AI use for OHS. RESULTS: In total, 29% reported firm-level AI use for OHS. Larger-sized firms and those with hybrid work arrangements had a greater odds of AI use for OHS. Also, firms with high workplace hazard exposure had a greater odds of AI OHS use. More positive perceptions of AI's impact on OHS were associated with firm-level AI use for OHS. CONCLUSIONS: AI use for OHS may be concentrated among hazardous firms and those with the conditions to support technological adoption. Research examining AI's effectiveness in OHS settings is needed to guide evidence-based implementation in occupational health practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".