Potential barriers to engineered noise control in food and beverage manufacturing in British Columbia, Canada: A qualitative study
Bibliographic record
Abstract
OBJECTIVE: Noise is probably the most ubiquitous of occupational hazards. While many jurisdictions require hearing conservation programs (HCP), the most effective intervention-engineered noise controls (ENC)-is rarely implemented. We used a qualitative study design to investigate barriers to the implementation of ENC. DESIGN & STUDY SAMPLE: Fifty-five individuals at eight food and beverage manufacturers participated. In-depth interviews were conducted and analysed using grounded theory techniques. HCP audits provided contextual information. RESULTS: None of the companies had fully implemented HCP as required by regulation. Many factors emerged as possible barriers to the implementation of engineered noise control, including: poor knowledge of relevant regulations, noise reduction options and the health impacts of noise; weak technical skills and experience; low ranking of noise as a hazard by stakeholders; issues around job insecurity, weak language skills; lack of 'quiet' machine options and information from equipment manufacturers; poor employer-regulator relationships; barriers to employee-employer reporting; informal valuation of ENC costs; and feasibility issues. CONCLUSIONS: Potential barriers to the implementation of ENC were identified, and classified at three levels at which they operated. Many barriers could be addressed by a more rigorous application of existing HCP regulation and improvements in education, technical support, and regulatory enforcement.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".