Challenges and Strategies Related to Hearing Loss Among Dairy Farmers
Bibliographic record
Abstract
CONTEXT: Farming is often imagined to be a serene and idyllic business based on historical images of a man, a horse, and a plow. However, machinery and equipment on farms, such as older tractors, grain dryers, and vacuum pumps, can have noise levels, which may be dangerous to hearing with prolonged, unprotected exposure. PURPOSE: This qualitative study in Ontario, Canada, explored the challenges and coping strategies experienced by dairy farmers with self-reported hearing loss and communication difficulties. Through in-depth interviews, 13 farmers who experience significant hearing loss were questioned about the challenges they face as a result of hearing loss and the strategies they use to overcome or compensate for problems. FINDINGS: The 2 major challenges encountered by dairy farmers with a hearing loss were: (1) obtaining information from individuals, within groups, and through electronic media; and (2) working with animals, machinery, and noise. To cope with these challenges, participants used strategies identified as problem and emotion focused. CONCLUSIONS: Four themes arose from analysis of the challenges encountered and strategies used: 1. Hearing loss is experienced as a "familiar," but "private," problem for dairy farmers. 2. Communication difficulties can negatively affect the quality of relationships on the farm. 3. Safety and risk management are issues when farming with a hearing loss. 4. The management or control of excessive noise is a complex problem, because there are no completely reliable yet practical solutions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".