Translation and validation of the Child Three-Factor Eating Questionnaire (CTFEQr17) in French-speaking Canadian children and adolescents
Bibliographic record
Abstract
OBJECTIVE: To translate and validate the Child Three-Factor Eating Questionnaire (CTFEQr17), assessing cognitive restraint (CR), uncontrolled eating (UE) and emotional eating (EE), among French-speaking Canadian young individuals. DESIGN: Phase 1 comprised a translation and the evaluation of the comprehension of the questionnaire. Phase 2 comprised a confirmatory factor analysis (CFA), the evaluation of internal consistency (Cronbach's α), test-retest reliability (intra-class correlation coefficients (ICC)) and construct validity, including correlations among the CTFEQr17 and Eating Attitudes Test (EAT-26), anthropometrics, dietary intake and diet quality. SETTING: Primary and secondary schools, Québec City, Canada. PARTICIPANTS: Phases 1 and 2 included 20 (40 % boys, mean age 11·5 (sd 2·4) years) and 145 (48 % boys, mean age 11·0 (sd 1·9) years) participants, respectively. RESULTS: Phase 1 resulted in the questionnaire to be used in Phase 2. In Phase 2, the CFA revealed that the seventeen item, three-factor model (CTFEQr17) provided an excellent fit. Internal consistency was good (Cronbach's α: 0·81-0·90). Test-retest reliability was moderate to good (ICC = 0·59, (95 % CI 0·48, 0·70), ICC = 0·78, (95 % CI 0·70, 0·84), ICC = 0·50, (95 % CI 0·38, 0·62) for CR, UE and EE, respectively). CR correlated with EAT-26 score (r = 0·43, P < 0·0001). UE and EE correlated negatively with BMI Z-scores (r = -0;·26, P = 0·003; r = -0;·19, P = 0·03, respectively). CR correlated with the proportion of energy intake from protein and diet quality (r = 0·18, P = 0·04; r = 0·20, P = 0·02, respectively). CONCLUSION: The CTFEQr17 is suitable to use among French-speaking Canadian young individuals.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".