A qualitative study of the knowledge, attitudes, and behaviors of people exposed to diesel exhaust at the workplace in British Columbia, Canada
Bibliographic record
Abstract
PURPOSE: To identify exposure-related knowledge, attitudes and behaviors of individuals occupationally exposed to diesel exhaust (DE); to reveal strengths, knowledge gaps and misperceptions therein. METHODS: A Mental Models approach was used to gather information about current scientific understanding of DE exposure hazards and the ways in which exposure can be reduced. Thirty individuals in British Columbia who were regularly exposed to occupational DE were interviewed. The audio was recorded and transcribed. Data was grouped together and examined to draw out themes around DE awareness, hazard assessment and risk reduction behaviors. These themes were then compared and contrasted with existing grey and research literature in order to reveal strengths, gaps and misperceptions regarding DE exposure. RESULTS: Study participants were aware and concerned about their exposure to DE but had incomplete and sometimes incorrect understanding of exposure pathways, health effects, and effective strategies to reduce their exposures. The perceived likelihood of exposure to DE was significantly greater compared to that of other work hazards (p<0.01), whereas the difference for their perceived severity of consequences was not significant. There was no universally perceived main source of information regarding DE, and participants generally distrusted sources of information based on their past experience with the source. Most of the actions that were taken to address DE exposure fell into the area of administrative controls such as being aware of sources of DE and avoiding these sources. CONCLUSIONS: This study of the knowledge, attitude, and behavior of those occupationally exposed to DE found, most notably, that more education and training and the creation of a health effects inventory regarding DE exposure were desired.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".