Development and validation of the Perceived Food Environment Questionnaire in a French-Canadian population
Bibliographic record
Abstract
OBJECTIVE: The present study aimed to develop and validate a questionnaire assessing perceived food environment in a French-Canadian population. DESIGN: A questionnaire, the Perceived Food Environment Questionnaire, was developed assessing perceived accessibility to healthy (nine items) and unhealthy foods (three items). A pre-test sample was recruited for a pilot testing of the questionnaire. For the validation study, another sample was recruited and completed the questionnaire twice. Exploratory factor analysis was performed on the items to assess the number of factors (subscales). Cronbach's α was used to measure internal consistency reliability. Test-retest reliability was assessed with Pearson correlations. SETTING: Online survey. SUBJECTS: Men and women from the Québec City area (n 31 in the pre-test sample; n 150 in the validation study sample). RESULTS: The pilot testing did not lead to any change in the questionnaire. The exploratory factor analysis revealed a two-subscale structure. The first subscale is composed of six items assessing accessibility to healthy foods and the second includes three items related to accessibility to unhealthy foods. Three items were removed from the questionnaire due to low loading on the two subscales. The subscales demonstrated adequate internal consistency (Cronbach's α=0·77 for healthy foods and 0·62 for unhealthy foods) and test-retest reliability (r=0·59 and 0·60, respectively; both P<0·0001). CONCLUSIONS: The Perceived Food Environment Questionnaire was developed for a French-Canadian population and demonstrated good psychometric properties. Further validation is recommended if the questionnaire is to be used in other 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".