Translation and Validation of the Hospital for Special Surgery Pediatric Functional Activity Brief Scale for French Paediatric Populations
Bibliographic record
Abstract
Purpose: Physical activity level is a prognostic variable for patients with injuries. Self-report questionnaires exist to obtain these measures; however, they are not accessible to all populations because of language barriers. Therefore, the purpose of this study was to translate and validate the Hospital for Special Surgery Pediatric Functional Activity Brief Scale (HSS Pedi-FABS) for francophones. Method: We translated the HSS Pedi-FABS using the forward–reverse translation approach and validated it among bilingual adults and an expert panel before administering it to a paediatric population. A repeated-measures crossover design was used: forty-three middle school students, aged 13.1 (SD 0.75) years, were randomly administered either the English or the French questionnaire. Two days later, all participants completed the other version. The translated questionnaire was assessed for its convergent validity (Spearman’s r correlation coefficients [ r s ]), internal consistency (Cronbach’s α), and reliability (standard error of measurement [SEM]). Results: All assessments had a significance level of p < 0.001 with an excellent Spearman’s r correlation coefficient between the participants’ total scores on the translated questionnaires ( r s = 0.911). The overall scores for the questionnaire and the individual items of the questionnaire revealed excellent internal consistency (α = 0.868) and reliability (SEM = 0.334). Conclusions: The validated and reliable translated questionnaire can be used by researchers and clinicians to assess physical activity levels in French paediatric populations.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".