French-language adaptation of the 16D and 17D Quality of Life measures and score description in two Canadian pediatric samples
Bibliographic record
Abstract
Purpose The Health state descriptive system includes standardized self-administered instruments for measuring Health-Related Quality of Life (HRQoL) respectively among adolescents, and children. The objectives of the current study were: (1) to translate and adapt the pediatric-adolescent version 16D and 17D from English into French (Canada), (2) to demonstrate their feasibility in pediatric conditions.Methods The translation methodology combined forward and back translations, and cognitive debriefing with eight adolescents and eight children. Four bilingual translators were involved in the process. We administered the translated versions to two clinical samples, being treated for Primary immunodeficiency (PID, n = 48, aged 14.1 years, 20 girls), and having recovered from pediatric Acute Lymphoblastic Leukemia (ALL, n = 153, aged 14.7 years, 77 girls).Results Cognitive debriefing indicated that that the instructions, items, and response options were clear, easy to understand, and easy to answer. Adjustments were made for clarity. Translated versions were highly usable (measurement completion >90%). HRQoL levels were high for both samples (range 0.85–0.96). Participants reported lower levels if they were adolescents, particularly if they were girls. Older boys with PID reported a lower HRQoL than their counterparts with a history of ALL. PID and ALL patients mainly reported issues with discomfort and pain, concentration/learning, physical appearance, and psychological distress and sleeping, although to a different degree.Conclusion The French-language versions of the 16D and 17D are easy to administer and may be used to identify problematic domains. Greater availability of translated versions of short evaluation tools may facilitate broader uptake of screening practices in pediatric care.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.002 | 0.000 |
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".