Hearing loss in military aviation and other trades: investigation of prevalence and risk factors.
Bibliographic record
Abstract
INTRODUCTION: Continuous increase in the cost of claims for noise-induced hearing loss in Canadian Forces personnel prompted a review of hearing conservation practices. Investigations comprised retrospective analyses of hearing test results and a prospective survey of risk factors for hearing loss. METHOD: There were 1057 individuals working in 107 air, land, and sea trades who contributed their current and first hearing test results. Subjects completed a 64-item questionnaire relating to demographics, occupational and non-occupational noise exposure history, training in and use of hearing protection, and non-noise risk factors, including head injury, ear disease, medications, and solvent exposure. Subject recruitment, hearing assessment, and questionnaire distribution were carried out by military personnel. Apparatus and protocols for hearing testing conformed to current clinical practice. RESULTS: Prevalence of moderate to severe hearing loss progressed with age, with hearing thresholds in those over 45 yr ranging broadly from normal to over 70 dB HL. Unprotected exposure to noise and solvents appeared to be the likely determinates of adverse outcome. Head injury, ear disease, and the use of ototoxic medications had minimal impact. The data for individuals in 17 selected trades in the 3 environments modeled that of the total sample. DISCUSSION: Responses to the questionnaire suggested that training on noise hazard and selection and use of hearing protection were inadequate. Hearing protection was reported to be incompatible with other gear, uncomfortable, and an impediment to communication. Approaches for upgrading hearing conservation strategies, including the creation of end-user focus groups, were considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".