Social support for healthy eating: development and validation of a questionnaire for the French-Canadian population
Bibliographic record
Abstract
OBJECTIVE: The present study aimed to develop and validate a questionnaire assessing social support for healthy eating in a French-Canadian population. DESIGN: A twenty-one-item questionnaire was developed. For each item, participants were asked to rate the frequency, in the past month, with which the actions described had been done by family and friends in two different environments: (i) at home and (ii) outside of home. The content was evaluated by an expert panel. A validation study sample was recruited and completed the questionnaire twice. Exploratory factor analysis was performed on items to assess the number of subscales. Internal consistency reliability was assessed using Cronbach's ɑ. Test-retest reliability was evaluated with intraclass correlations between scores of the two completions. SETTING: Online survey. SUBJECTS: Men and women from the Québec City area (n 150). RESULTS: The content validity assessment led to a few changes, resulting in a twenty-two-item questionnaire. Exploratory factor analysis revealed a two-factor structure for both environments, resulting in four subscales: supportive actions at home; non-supportive actions at home; supportive actions outside of home; and non-supportive actions outside of home. Two items were removed from the questionnaire due to low loadings. The four subscales were found to be reliable (Cronbach's ɑ=0·82-0·94; test-retest intraclass correlation=0·51-0·70). CONCLUSIONS: The Social Support for Healthy Eating Questionnaire was developed for a French-Canadian population and demonstrated good psychometric properties. This questionnaire will be useful to explore the role of social support and its interactions with other factors in predicting eating behaviours.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
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".