“You Can Lead a Horse to Water …”: Focus Group Perspectives on Initiating and Supporting Hearing Health Change in Older Adults
Bibliographic record
Abstract
PURPOSE: The aim of this study was to use focus group discussions to (a) evaluate the use of an educational presentation as an impetus for hearing health change and (b) investigate hearing health from the perspective of older adults. METHOD: Twenty-seven (4 men, 23 women) community-dwelling older adults attended 4 data collection events. Participants attended a presentation titled Hearing Health in Older Adults, which was delivered by a trained presenter in a peer-teaching-peer format. Following each presentation, a focus group discussion took place. Digital audio recordings, field notes, and memos of the discussions were used to create verbatim transcripts. Data were analyzed using qualitative description and thematic analysis techniques. RESULTS: Five central themes emerged when older adult focus groups discussed the presentation and hearing health change: recognizing and admitting, understanding the options, sharing stories and experiences, barriers and facilitators, and the presentation. CONCLUSION: Facilitators to hearing health change identified by participants include widespread education about hearing health; clarification about roles, professional motivation, and cost in hearing care; and opportunities to learn from and share personal stories with peers.
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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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".