Psychosocial outcome measures for conductive and mixed hearing loss treatment: An overview of the relevant literature
Bibliographic record
Abstract
OBJECTIVE: To identify the psychosocial assessments utilized with individuals with conductive and/or mixed hearing loss as part of a broader effort by the Auditory Rehabilitation Outcomes Network (AURONET) group to develop a core set of patient-centred outcome measures. DESIGN: A review of articles published between 2006 and 2016 was completed. Included studies had more than three adult participants, were available in English, and reported a psychosocial outcome from any treatment of mixed and/or conductive hearing loss. STUDY SAMPLE: Sixty-six articles from seven databases. RESULTS: Sixty-six articles met our inclusion/exclusion criteria. Within this set, 15 unique psychosocial or patient-reported outcome measures (PROs) were identified, with the Abbreviated Profile of Hearing Aid Benefit (APHAB) and Glasgow Benefit Inventory (GBI) being the most frequently dispensed. Five of the fifteen were only administered in one study. In-house questionnaires (IHQs) were reported in 19 articles. CONCLUSIONS: Only 66 (22%) of the 300 articles with outcomes contained a PRO. Some of the mostly frequently employed PROs (e.g., APHAB) were judged to include only social items and no psychological items. Lack of PRO standardization and the use of IHQs make psychosocial comparisons across treatments in this population difficult for patients, clinicians and stakeholders.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".