Validation of the “Cooking and Food Provisioning Action Scale” for the French-Canadian adult population and association with diet quality
Bibliographic record
Abstract
Food literacy is a set of interrelated knowledge, skills, and behaviors required to plan and manage, select, prepare, and eat food, which can be measured by the “Cooking and Food Provisioning Action Scale” (CAFPAS). This study aimed to validate the French translation of the CAFPAS and to explore the association between food literacy and diet quality. The French translation of the CAFPAS was completed within the NutriQuébec project, a web-based cohort in Quebec, Canada. Cronbach's alpha was used to evaluate the internal consistency of the scale. Associations between the total CAFPAS score and sociodemographic variables were explored to assess the construct validity of the scale. Diet quality was assessed with the Healthy Eating Food Index-2019 (HEFI-2019) and its association with food literacy was analysed. A total of 1204 participants (87.8 % women) were included. Cronbach's alpha values were equal to or higher than 0.85 for the total CAFPAS score and its three subscales. The total CAFPAS score was significantly associated with age ( p < 0.0001), household annual income ( p = 0.0076), and gender ( p = 0.0014), with women showing a higher level of food literacy than men. The total CAFPAS score was positively but weakly correlated with the total HEFI-2019 score ( r = 0.12; p = 0.0001). Globally, the French translation of the CAFPAS appeared to be a valid and reliable scale to assess food literacy in French-speaking adults in Canada. • Food literacy is important to cope with our complex food environment. • Cooking and Food Provisioning Action Scale (CAFPAS) can assess food literacy. • The French translation of the CAFPAS appeared to be a valid and reliable scale. • Food literacy was associated with age, household annual income, and gender. • Food literacy was weakly but significantly associated with diet quality.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 0.001 |
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".