Prevalence of hearing problems among Canadian Armed Forces Veterans: Life After Service Studies
Bibliographic record
Abstract
Introduction: Although hearing impairment is a known hazard of military service, there is limited prevalence data for Canadian Armed Forces (CAF) Veterans. Using two self-report methods in the 2010 and 2013 Life After Service Studies (LASS), this study compared hearing problem prevalence in Veterans and in the general Canadian population. Methods: Self-reported hearing problems were measured in Regular Force Veterans using a question adapted from the Participation and Activity Limitation Survey (PALS) in LASS 2010 and the Health Utilities Index Mark 3 (HUI3) in LASS 2013. Prevalence was compared to the general population using the 2013 Canadian Community Health Survey (CCHS) and to Veterans Affairs Canada audiometry-based disability benefits assessment for service-related hearing loss and tinnitus. Results: Hearing problem prevalence was 27.8% (26.3–29.4%) using the adapted PALS question in 2010 and 8.5% (7.4%–9.8%) using the HUI3 module in 2013. The prevalence of hearing problems in the general population using HUI3 after adjusting CCHS data for age and sex to match the Veterans was 2.0% (1.8–2.2%). Hearing problem prevalence in those aged 20–49 was higher in Veterans using PALS (21.1%, 19.4–23.0%) and HUI3 (4.7%, 3.6–6.3%) than in the general Canadian population (1.0%, 0.7–1.3%). Discussion: Self-reported hearing problems are more prevalent in CAF Veterans than the general population, prevalence varies considerably with the measurement instrument used. Veterans who did not have disability benefits for ear diagnoses reported hearing problems. Implications are discussed for services and research aimed at the prevention, mitigation, and measurement of hearing loss in this at-risk population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".