Certification in hazardous industries : an evaluation of the British Columbia Faller Training Standard
Bibliographic record
Abstract
Manual tree falling is historically one of the most hazardous occupations in British Columbia (BC). In an effort to reduce injury rates in this trade, WorkSafeBC implemented regulations requiring mandatory safe work practice training and certification known as the BC Faller Training Standard (BCFTS). The goal of this thesis was to assess if mandatory certification is associated with changes in injury rates and risk of injury for manual tree fallers. Two studies were conducted to determine effects of certification: 1) a comparison of manual tree faller specific injury rates for each forest region of BC during pre and post certification periods and 2) a comparison of the individual level risk of injury associated with certification status for a cohort of manual tree fallers. Annual injury rates representing the number of injuries/10,000,000m³ wood for each BC forest region were constructed using workers’ compensation data obtained from WorkSafeBC and wood harvest volume data from the Ministry of Forests and Range. The individual level relative risk (RR) of injury associated with certification status was determined using WorkSafeBC workers compensation data linked to a cohort listed in the BC Forest Safety Council faller registry. The injury rate study indicated that following implementation of mandatory safe work practice certification, acute injury rates in the Coast Forest Region dropped from 33.5 injuries/10,000,000m³ in 2006 to 25.8 injuries/10,000,000m³ in 2008. The cohort study based on experienced fallers who obtained grandfathered certification found that individual level risk of injury did not change significantly (RR=1.11, 95%CI 0.58,2.14) for the year immediately following certification compared to the year before certification. Combining these two study outcomes suggests the BCFTS may have an effect in the region of the province with the highest level of manual tree falling, although the individual level risk analysis suggests that the effects may take longer to be realized in the industry as a whole.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".