Factors influencing respirator use at work in respiratory patients
Bibliographic record
Abstract
BACKGROUND: When engineering controls such as ventilation are not sufficient to prevent hazardous exposures in workplaces, respiratory protective devices (RPDs) may be provided to decrease workers' exposures. Often, workers do not use RPDs consistently when required. AIMS: Our goal was to determine important factors associated with RPD usage in workers with respiratory disease exposed to airborne hazards at work. METHODS: One hundred and twenty-nine respiratory clinic patients in jobs with self-identified hazardous airborne substances completed a questionnaire and their clinic files were reviewed. Statistical analysis using chi-squared test and binary logistical regression was done to identify associations with RPD usage. RESULTS: Forty-one per cent reported always wearing RPDs whenever a hazard was present; 33% never wore RPD. Compliance was highest among healthcare workers (72%) and lowest among workers in food and service industries (13 and 22%, respectively), P < 0.01. The compliance of co-workers, conveniently located RPDs, safety training discussing the use of RPDs, fit testing available at the workplace and age were positively associated with compliance (P < 0.05). Experiencing symptoms of shortness of breath and nasal stuffiness were negatively associated with compliance (P < 0.05). CONCLUSIONS: Addressing company factors and workers' symptoms apparently influencing compliance may optimize RPD usage.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".