Noise as an explanatory factor in work-related fatality reports
Bibliographic record
Abstract
Noise exposure in the workplace is a common reality in Québec, Canada as it is elsewhere. However, the extent to which noise acts as a causal or contributive factor in industrial work-related accidents has not been studied thoroughly despite its plausibility. This article aims to describe the importance or potential importance, during investigations looking into the specific causes of each work-related fatal accident, of noise as an explanatory factor. The written information contained in the accident reports pertaining to contextual and technical elements were used. The study used multiple case qualitative content analysis. This descriptive study was based on the content analysis of the 788 reports from the Commission de la santé et de la sécurité du travail du Québec [Workers' Compensation Board (WCB)] investigating the fatal work-related accidents between 1990 and 2005. The study was descriptive (number and percentages). Noise was explicitly stated as one of the explanatory factors for the fatal outcome in 2.2% (17/788) of the fatal accidents, particularly when the work involved vehicular movement or the need to communicate between workers. Noise was not typically considered a unique cause in the accident, notably because the investigators considered that the accident would have probably occurred due to other risk factors (for example, disregard of safety rules, shortcomings in work methods, and inadequate training). Noise is an important risk factor when communication is involved in work. Since noise is ubiquitous and may also interfere with vigilance and other risk factors for accidents, it may be a much more important contributing factor to accidents than is currently recognized.
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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 | 0.010 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".