Canadian French translation and linguistic validation of the child health utility 9D (CHU9D)
Bibliographic record
Abstract
BACKGROUND: Several preference based measures are validated for adults in cost utility analysis, but less are available for children and many researchers have criticized the quality of pediatric economic studies. The objective of this study was to perform a Canadian French translation and linguistic validation of the Child Health Utility 9D (CHU9D) that was conceptually equivalent to the original English version for use in Canada. METHODS: The translation and linguistic validation were realized by ICON Clinical Research (UK) Limited in association with the developer of the CHU9D and Canadian collaborators. This was done in accordance with industry standards and the guidance of the Food and Drug Administration (FDA) for patient-reported outcome (PRO) instruments. Five steps were considered: concept elaboration; forward translation; back translation; linguistic validation; proofreading and final verification. RESULTS: The CHU9D Canadian French translation and linguistic validation were realized without any major difficulties. Only 3 changes were made after the forward translation and 5 after the back translation. The result of back translation was very similar to the original English version. Six additional changes suggested by the developer team were accepted and the linguistic validation with five children led to 2 additional changes. Most changes were generally to change one word to better sounding Canadian French. CONCLUSION: We produced a Canadian French translation and cross-cultural adaptation of the Child Health Utility 9D (CHU9D). Before being used in clinical settings and research projects, the final Canadian French translation needs to be validated for metrological qualities of reliability and validity.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".