Obtaining compliance with occupational health and safety regulations: a multilevel study using self-determination theory
Bibliographic record
Abstract
It was hypothesized that occupational health and safety (OHS) inspectors who prefer to use autonomy supportive tactics to resolve workplace conflicts (e.g. providing rationale, choices) would be more effective in resolving industry non-compliance with OHS regulations, compared to inspectors who prefer to use coercive tactics (e.g. deadlines, pressure). Preferences for resolving work conflicts were collected from 39 Canadian OHS inspectors and were linked to administrative records documenting 17,960 industry inspection episodes and 29,451 compliance orders issued by those inspectors from 2003-2006. Multilevel Poisson and negative binomial regression models examined associations between inspector autonomy-supportiveness and compliance outcomes, adjusting for covariates at the inspector level (e.g. job experience, number of inspection episodes) and at the worksite level (e.g. workplace safety record). Relative to coercive inspectors, autonomy-supportive inspectors issued fewer severe compliance orders and achieved compliance after fewer worksite visits. Use of autonomy-supportive approaches may reduce exposure to preventable injuries at non-compliant worksites.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".