Hearing Specific and Generic Measures of the Psychosocial Impact of Hearing Aids
Bibliographic record
Abstract
Hearing-specific and generic measures of hearing aid outcome were examined in order (a) to determine their relative sensitivity to hearing aid use and (b) to examine the relationship between pre–hearing aid use expectations and post-use outcomes. Ninety-two hearing-impaired individuals completed some combination of the Abbreviated Profile of Hearing Aid Benefit, Expected Consequences of Hearing Aid Ownership (ECHO), Satisfaction with Amplification in Daily Life (SADL), and Psychosocial Impact of Assistive Devices Scale, and provided reports of their daily and lifetime hearing aid use. In general, (a) the longer individuals wear hearing aids, the more positive the reported outcome, and (b) ECHO scores of non–hearing aid users are higher than SADL scores of new hearing aid users (six weeks to one year of use) but are similar to those obtained from experienced users (greater than one year of use). Between-questionnaire comparisons showed the generic measure to be as sensitive as the hearing aid specific measures. We suggest that generic measures have some advantages over hearing specific measures but that each has a place in the clinic. Se examinaron medidas genéricas y específicas de la audición en el rendimiento de auxiliares auditivos, para (a) determinar la sensibilidad relativa al uso del auxiliar auditivo, y (b) examinar la relación entre expectativas previas al uso del auxiliar auditivo y resultados posteriores a su utilización. Noventa y dos individuos hipoacúsicos completaron una combinación del Perfil Abreviado de Beneficio del Auxiliar Auditivo, Consecuencias Esperadas de la Posesión de un Auxiliar Auditivo (ECHO), Satisfacción y Amplificación en la Vida Diaria (SADL), y la Escala del Impacto Psico-social de Dispositivos de Ayuda, aportando reportes del uso de su auxiliar auditivo diariamente y a lo largo de toda su vida. En general, (a) cuanto más tiempo han usado el auxiliar auditivo los individuos, tanto más positivo es el resultado reportado, y (b) los puntajes ECHO de seis no usuarios de audífono fueron mayores que los puntajes SADL de los nuevos usuarios (seis semanas a un año de uso). Las comparaciones entre cuestionarios mostró que las medidas genéricas eran tan sensibles como las medidas específicas del auxiliar auditivo. Sugerimos que las medidas genéricas tienen ciertas ventajas sobre las medidas específicas de audición, pero cada una tiene su lugar en la clínica.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".