<i>Vegetable and Fruit Intake</i> And Factors Influencing their Intake
Bibliographic record
Abstract
PURPOSE: A food frequency questionnaire (FFQ) and an attitude/behaviour questionnaire (ABQ) were developed, and their validity and reliability were tested to determine adolescents' vegetable and fruit (VF) intakes and factors influencing their food choices. METHODS: High school students living in the lower mainland of British Columbia participated. The FFQ was adapted from the National Cancer Institute VF By-Meal screener, which was designed to be used with adults. After several focus groups with adolescents, the FFQ was revised to make it more user-friendly. The ABQ was developed after a literature review of factors influencing youth VF intake, and was based on the constructs within the Health Belief Model. RESULTS: The FFQ was validated against a written 24-hour dietary record (correlation coefficient = 0.52). The test-retest reliability coefficient for the FFQ was 0.46. A panel of experts tested the ABQ construct validity, and changes were made as a result of the recommendations. Internal consistency reliability and test-retest reliability of the ABQ were 0.71 and 0.59, respectively. Both questionnaires were tested for face validity with students and revised accordingly. CONCLUSIONS: Further validation of these two questionnaires against other standardized tools is required. Future studies with adolescents using these tools can guide program and resource development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".