International Classification of Functioning, Disability and Health as a Foundation for Instrument Development: Translation and Field Testing of the Swedish Version of the Hearing and Functioning in Everyday Life Questionnaire
Bibliographic record
Abstract
PURPOSE: The aim of the current study was to translate the English original version of the Hearing and Functioning in Everyday Life Questionnaire (HFEQ) to Swedish, evaluate the translation in terms of comprehensibility and relevance from a patient perspective, and culturally adapt the Swedish version (HFEQ-SWE). METHOD: A six-step methodology including translation into Swedish according to best practice methodology, evaluation, and cultural adaptation of the HFEQ-SWE using qualitative focus group interviews was used in this study. Thirteen adults with hearing loss were recruited from the audiological clinic in Örebro, Sweden. Their mean age was 57 years, and the four-frequency pure-tone average for the best ear ranged from 5 to 115 dB HL. RESULTS: The results demonstrated that most of the items in the HFEQ-SWE were viewed as relevant by the informants. Not all items were considered relevant for all people, which also reflect the individual variability of everyday functioning. Most items were recognized as comprehensible without any changes needed. CONCLUSIONS: Overall, the results indicate that the translation is semantically and culturally valid from the patient's perspective. Some revisions were suggested that need to be addressed in the further validation process of the HFEQ, and one item was removed.
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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 | 0.031 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".