“Something is just not right with my hearing”: early experiences of adults living with hearing loss
Bibliographic record
Abstract
To understand the psychosocial process of how adults experience hearing loss; specifically, their readiness to accept that they may have hearing loss, and the challenges and coping strategies associated with it. A grounded theory methodology guided the research. A patient-orientated research approach informed the study. Thirty-nine individual interviews and six focus groups were completed. Participants included 68 individuals aged 50 years and older with self-reported hearing loss living in Newfoundland and Labrador. The theoretical construct, ‘Realising that something is just not quite right with my hearing’ captured individuals’ experiences as they gradually awakened to the fact that they had hearing loss. Three categories describe the process: (1) Rationalising suspicions, (2) Managing the invisible and (3) Reaching a turning point. Many individuals do not recognise hearing loss in its early stages, although they may be already experiencing its negative effects. It is important to identify motivators to engage individuals as early as possible in their hearing health. Taking a proactive approach to hearing health can help mitigate the potential negative outcomes of hearing loss.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".