Accountability: A Canadian municipal examination on the impact of vehicle and equipment training programs on preventable collisions with municipal fleet assets
Bibliographic record
Abstract
Annually, close to 1,000 Canadian workers die due to work-related injuries, exposures and disease. A lack of training and skills can contribute to this loss of life for many. Employers of all kinds, including municipalities, have a responsibility to protect workers and reduce risk to the organization through training activities. Known hazards, like the operation of vehicles and equipment require training before a worker can be deemed competent. Inevitably, preventable and non-preventable collisions will occur involving fleet vehicles and equipment. Documenting and analyzing preventable collisions as part of a collision investigation program, can help organizations understand why preventable collisions are occurring and how they may prevent further accidents. Survey respondents for this research study revealed that as a municipal fleet grows in size (more than 1000 fleet assets) and as municipal populations grow, municipalities are more likely to use a centralized and consistent training program. Further, municipalities with centralized and consistent training programs are more likely than municipalities with other training types to have a collision investigation program. Lastly, municipalities who provided preventable collision data through the survey, six of seven use a centralized training approach. This research reveals that municipalities are not measuring the impact and effectiveness of vehicle and equipment training programs. Additionally, municipalities are not using collision related data to understand why preventable collisions are occurring, influence training programs and reduce risk in meaningful ways. The costs of collisions is not being documented across the silos that exist in the municipal organizational structure. Lastly, from a policy perspective, requirements for fleet operators to document and analyze collision data should be a baseline expectation to protect their drivers and others sharing the road. Ultimately more research and data is required to support municipalities across Canada and the vehicle and equipment training programs they offer.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".