Embodying hearing loss: confronting the issue and adjusting to a new norm
Bibliographic record
Abstract
OBJECTIVE: To understand how older adults living with hearing loss cope with and manage their hearing loss. DESIGN: Grounded theory methodology with a patient-oriented approach was used to generate a theory grounded in data obtained from older adults living with hearing loss. Constant comparative analysis was used. STUDY SAMPLE: Participants included 68 individuals aged 50 years and older with self-reported hearing loss living in Newfoundland and Labrador, Canada. RESULTS: We report on two theoretical constructs of the psychosocial process of embodying hearing loss. 'Confronting the issue' captures individuals' experiences after they reached a turning point in acknowledging their hearing loss journey and began searching for services and supports that includes (1) accessing services and supports, (2) receiving the diagnosis, and (3) teasing out options. 'Adjusting to a new norm' describes participants' experiences as they started to navigate their life with hearing loss that includes (1) benefits and challenges living with a hearing assistive device, (2) developing coping strategies, and (3) envisioning a future living with hearing loss. CONCLUSIONS: Embodying hearing loss includes both understanding the concept of hearing loss and engaging with the challenges, emotions and experiences associated with it to help promote understanding, support and inclusion in society for those affected by hearing loss.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".