Development, validity, and reliability assessment of the Canadian Food Literacy Measure
Bibliographic record
Abstract
Food literacy is a growing area of interest given its potential to support healthy and sustainable diets. Most existing food literacy measures focus on nutrition and food skills but fail to address food systems and socio-environmental aspects of food literacy. Further, measures developed and tested in the Canadian context are lacking. The objective of this project was to develop and test the validity and reliability of a brief self-administered measure, in French and English, designed to assess multiple dimensions of food literacy among adults living in Canada. The 23-item Canadian Food Literacy Measure was developed through an iterative process that included assessment of face and content validity through expert review ( n = 20) and cognitive interviews ( n = 20) and construct validity and reliability, i.e., internal consistency through an online survey ( n = 154). The results indicate that the measure is well understood by both English- and French-speaking adults. The measure’s construct validity is demonstrated by the observed differences in total scores in hypothesized directions by gender ( p = 0.003), age ( p = 0.007), education level ( p = 0.002), health literacy ( p < 0.001) and smoking status ( p = 0.001), and the significant positive correlation ( r = 0.29; p = 0.002) between total scores and fruit and vegetable intake. The measure also has high internal consistency with a Cronbach’s coefficient alpha of 0.80. This measure can be used in surveillance studies to provide insight into the food literacy of adults living in Canada and in epidemiologic research that aims to explore how food literacy is associated with a variety of health outcomes.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".