French-Canadian translation and validation of four questionnaires assessing hearing impairment and handicap
Bibliographic record
Abstract
OBJECTIVE: Questionnaires evaluating hearing impairment are available in English but there is a need for French standardised questionnaires for researchers as well as for audiologists and other clinicians. The objective of this study is to describe the translation and validation of four questionnaires that assess different aspects of hearing impairment and handicap among elders with hearing loss, by comparing the main score and psychometric evaluation of original and French-Canadian (FC) versions of the World Health Organization Disability Assessment Scale II (WHO-DAS II), the Screening Test for Hearing Problems (STHP), the Abbreviated Profile of Hearing Aid Benefit (APHAB) and the Measure of Audiologic Rehabilitation Self-Efficacy for Hearing Aids (MARS-HA). DESIGN: Vallerand method: translation and back-translation by two translators, revision by a committee of experts and pre-tested with five bilingual older participants. STUDY SAMPLE: Participants (n = 29) were 65 years of age or older including 21 with hearing aids. RESULTS: The psychometric properties (internal consistency, temporal stability after four weeks) indicate good reliability for most of the translated questionnaires and their subscales, especially the WHO-DAS II. CONCLUSIONS: The translations in FC of two hearing loss and two hearing aid questionnaires were validated. It is recommended to pursue the demonstration for temporal stability for the STHP.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".