Quebec French Translation, Cultural Adaptation, and Validation of the Singing Voice Handicap Index-10 Questionnaire for Singers with Dysphonia
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Dysphonia is a common voice disorder that can significantly impact a person's life; it requires a collaborative evaluation by both speech-language pathologists and otolaryngologists that takes the patient's perspective into account. The aim of this study was to translate and culturally adapt the Singing Voice Handicap Index questionnaire (SVHI-10), a reliable patient-reported outcome evaluation tool for dysphonia, for the Quebec French population. The result is the Singing Voice Handicap Index-10-QC (SVHI-10-QC). STUDY DESIGN: This study was a prospective translation and validation process. METHODS: The translation process complied with international recommendations and followed a standard forward-backward translation procedure and cognitive debriefing with 10 singers. The Quebec French version was administered to two study samples: 30 vocal professionals with no dysphonia and 53 vocal professionals with dysphonia as one of their primary complaints. The SVHI-10-QC was assessed for construct validity, internal consistency, discriminatory capacity, and test-retest reliability. RESULTS: The SVHI-10-QC is valid, reliable, and ready for use with singer-patients suffering from dysphonia. CONCLUSIONS: The SVHI-10-QC is a reliable and valid tool for assessing the impact of dysphonia on French Quebec singers' quality of life.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".