Canadian French translation and linguistic validation of the health-related quality of life utility measure for pre-school children
Bibliographic record
Abstract
BACKGROUND: There is a need to perform a Canadian French translation and linguistic validation of the health-related quality of life utility measure for pre-school children (HuPS) conceptually equivalent to the original Canadian English version. RESEARCH DESIGN AND METHODS: The translation process consisted of forward and back translations. The linguistic validation was performed with the parents of preschool children during face-to-face cognitive debriefing interviews. The whole process was done in accordance with academic standards and the guidance of the Food and Drug Administration (FDA) for patient-reported outcome instruments. RESULTS: The results of back translations indicated that 89% of the sentences were identical or almost identical to the original English-language wording. The review of the back translations led to a change in 13 sentences out of 91 from the reconciled forward translation, while the linguistic validation process with 13 parents led to 14 additional changes. Preliminary reliability validation results indicate a Cronbach's alpha of 0.73. CONCLUSION: The translation and linguistic testing processes were successful in creating a valid HuPS in Canadian French (HuPS-CF). This translation should be the subject of reliability and validity studies in a wide variety of clinical and general populations before to use in research projects.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".