Associations between cooking skills, cooking with processed foods, and health: a cross-sectional study
Bibliographic record
Abstract
To improve health outcomes, home cooking has been suggested as a solution to reduce intakes of processed foods. However, little is known about how cooking skills or cooking with processed foods influence health. This cross-sectional study examined associations between diet and health outcomes with cooking skills and cooking with processed foods. The dataset included a nationally representative sample of 18 460 adults from Canadian Community Health Survey (CCHS) annual component rapid response modules on food skills. In the CCHS rapid response modules, diet and health outcomes (fruit and vegetable intake, general health, mental health, and obesity) and data related to cooking skills and cooking with processed foods were collected through self-report. Separate logistic regression models were fitted for each outcome, controlling for age, income, and education, and stratified by sex. Adults with poor cooking skills were less likely to have adequate fruit and vegetable intake (≥5 servings per day) ( p < 0.001), very good general health ( p < 0.001) or mental health ( p < 0.001), and obesity ( p = 0.02) compared to advanced cooking skills. Adults who cooked with highly processed foods were less likely to have adequate fruit and vegetable intake ( p < 0.001), very good general health ( p = 0.002) or mental health ( p < 0.001), but more likely to have obesity ( p = 0.03) compared to cooking with minimally processed foods. Cooking skills alone appear insufficient to protect against obesity. Results suggest that not only are cooking skills important, but the quality of ingredients also matter. Limiting the use of processed foods in addition to improving cooking skills are potential intervention targets to promote better health and diet 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".