Validation of the French-Canadian Translation of the ecSatter Inventory 2.0 in an Adult Sample
Bibliographic record
Abstract
OBJECTIVE: Evaluate the psychometric proprieties of the French-Canadian translation of the Satter Eating Competence Inventory (FrCanada ecSI 2.0). DESIGN: Cross-sectional validation study. PARTICIPANTS AND SETTING: 424 French-Canadian adult Facebook users (61.8% women, 96.0% White). VARIABLES MEASURED: Eating competence and variables related to eating or body image. ANALYSIS: Factor analyses to assess the structural validity. Cronbach α and intraclass correlation coefficient to estimate reliability. Chi-square test of independence, Student t test, and Pearson's correlations to assess construct validity. RESULTS: The mean eating competence score was 33.0 ± 7.8; 62.0% of participants were considered competent eaters (total score ≥ 32/48). The original 4-factor structure was not reproduced (unsatisfactory fit indices and/or factor loadings). Therefore, it is recommended to use the global score-but not the subscale scores-of the FrCanada ecSI 2.0. The questionnaire showed good internal consistency (Cronbach α = 0.86) and test-retest reliability (intraclass correlation = 0.81). Competent and noncompetent eaters differed according to gender (39.5% vs 27.3% male; P = 0.03), age (49.3 ± 13.6 vs 42.7 ± 14.2 years; P < 0.01), education (62.3% vs 50.6% with a university degree; P = 0.03), intuitive eating (3.6 ± 0.5 vs 3.1 ± 0.6; P < 0.001), cognitive restraint (12.3 ± 3.3 vs 13.8 ± 3.7; P < 0.001), and body esteem (3.3 ± 0.8 vs 2.5 ± 0.8; P < 0.001). CONCLUSION AND IMPLICATIONS: Results suggest that the FrCanada ecSI 2.0 is a valid and reliable tool to measure eating competence in French-Canadian adults.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".