Personal listening habits and the potential for hearing loss of Canadian university students
Bibliographic record
Abstract
Rationale: Personal and external factors, such as earphone type and music preference, can influence music volume adjustment beyond safe levels. The present study attempted to identify which factors are most influential on volume adjustment. Method: A cross-sectional survey of university students (n = 75) who use personal listening devices (PLD) was performed. Additionally, each participant's PLD music volume was measured through their earphones. Results: On average, participants listened to music at safe (<85 dB) but high levels (79.8 dB) for generally less than four hours per day. Nearly 60% of respondents used earbuds and half preferred “noisy” music genres such as hip-hop and rock/folk. The vast majority of respondents indicated listening to music while travelling by bus for the purpose of blocking out environmental noise or out of boredom. About 75% of the participants were categorized as “pro-noise”. Most students claimed to respond to changing noise environments by adjusting music volume, but few enabled PLD built-in volume controls. Impact: This study determined that earphone type, listening environment, music genre, and listening duration were influential on an participants’ adjustment of music volume. Further research is needed to assess earphone quality and to clearly elucidate more complex associations between external or personal factors and volume adjustment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".