Prevention in dangerous industries: does safety certification prevent tree-faller injuries?
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate if safety certification reduces the risk of work injury among experienced manual tree-fallers. METHODS: This study used a retrospective cohort study design. Experienced manual tree-fallers employed in the Canadian province of British Columbia (N=3251) between 2003-2008 were enumerated from a mandatory faller registry. Registry records with demographic and certification data were linked to workers' compensation claims for injury outcomes. Data were analyzed using discrete time survival analysis over a two-year period, centered on certification date with pre- and post-certification demarcated into four three-month periods. Models were adjusted for demographic, occupation/industry, previous injury, and seasonal/temporal effects. RESULTS: The relative risk (RR) of work injury during the post certification periods were elevated in comparison to the pre-certification reference period, but the 95% confidence intervals included "1" for all estimates by the end of follow-up, suggesting no statistically significant increased risk of injury. Results were consistent across different outcome measures of acute injury (ie, fracture or amputations) (N=186), musculoskeletal injury (ie, back strain) (N=137), and serious injury claims (ie, long duration, high cost and/or fatal) (N=155). CONCLUSION: Certification did not reduce the risk of work injury among experienced tree-fallers in the province of British Columbia. Non-statistically significant increases in the observed risk of work injury in the months immediately following certification may be attributable to an intervention effect or a methodological limitation related to a lack of individual-level, time-at-risk exposure data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".