Hearing Screening Program Impact on Noise Reduction Strategies
Bibliographic record
Abstract
The objective of this study was to determine the impact of the New South Wales Rural Hearing Conservation Program on the implementation of personal hearing protection (PHP) and noise management strategies among farmers who had participated in this program in New South Wales, Australia. A follow-up survey of a random sample of people screened through the New South Wales Rural Hearing Conservation Program was linked to their baseline data. The use of PHP at baseline was compared to use at follow-up in four specific scenarios: use with non-cabbed tractors, with chainsaws, with firearms, and in workshops. For non-cabbed tractors, the net gain in PHP use was 13.3%; the net gain was 20.8% for chainsaws, 6.7% for firearms, and 21.3% for workshops. Older farmers and those with a family history of hearing loss were less likely to maintain or improve PHP use. Those with severe hearing loss, males, and participants reporting hearing problems in situations where background noise was present were more likely to maintain or improve PHP use. Forty-one percent of farmers had initiated other strategies to reduce noise exposure beyond the use of PHP, which included engineering, maintenance, and noise avoidance solutions. The early (hopefully) identification of hearing deficit in farmers and farm workers can help promote behavior change and help reinforce a farm culture that supports hearing conservation. The continuation and expansion of hearing screening programs such as these should be encouraged as basic public health strategy in farming communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".