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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".