Construct validity and reliability of the Canadian Eating Practices Screener to assess eating practices based on 2019 Canada's Food Guide recommendations
Bibliographic record
Abstract
For the first time since its introduction, the 2019 Canada's Food Guide (2019-CFG) highlighted specific guidance on eating practices, i.e., recommendations on where, when, why, and how to eat. The Canadian Eating Practices Screener / Questionnaire court canadien sur les pratiques alimentaires was developed to assess eating practices based on the 2019-CFG healthy eating recommendations. The objective of this cross-sectional study was to assess the construct validity and reliability of the Canadian Eating Practices Screener. From July to December 2021, adults ( n = 154) aged 18–65 years completed a sociodemographic questionnaire and the screener. Construct validity was assessed by examining variability in screener scores, by comparing screener scores among subgroups with hypothesized differences in eating practices, and by examining the correlation between screener scores and fruit and vegetable intake. Reliability, i.e., internal consistency, was assessed by calculating Cronbach's coefficient alpha. Screener item scores were summed to provide a total score ranging from 21 to 105. The mean screener score was 76 (SD = 8.4; maximum, 105), ranging from 53 (1st percentile) to 92 (99th percentile). Differences in total scores in hypothesized directions were observed by age ( p = 0.006), perceived income adequacy ( p = 0.09), educational attainment ( p = 0.002), and smoking status ( p = 0.09), but not by gender or health literacy level. The correlation between screener scores and fruit and vegetable intake was 0.29 ( p = 0.002). The Cronbach's coefficient alpha was 0.79, suggesting acceptable to high internal consistency. Study findings provide preliminary evidence of the screener's construct validity and reliability, supporting its use to assess eating practices based on the 2019-CFG healthy eating recommendations.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".