Meal planning as a strategy to support healthy eating
Bibliographic record
Abstract
Meal planning is cited in the latest version of Canada’s Dietary Guidelines as one of four important food skills that help individuals choose, purchase and prepare healthy snacks and foods on a regular basis for themselves and members of their household. While meal planning is often mentioned as a strategy to overcome the main barrier to healthy eating, lack of time, it may also assist individuals reduce stress related to mealtimes and increase frequency of family meals. Although, there is relatively sparse literature that meal planning confers benefits to the diet, there is a history of evidence indicating that it helps manage dietary restrictions related to specific diseases (e.g., diabetes), which can translate into helping the general public consume more fruits and vegetables, while consuming fewer processed foods. In 2013 Health Canada implemented a one-year communication campaign to promote meal planning to Canadian parents as a strategy to increase home-based food preparation and family meals. The campaign evaluation found that awareness was associated with greater odds of having more positive attitudes towards meal planning. However, more than half of parents also reported that lack of time was a major barrier for meal planning. Dietitians can recommend meal planning as a viable strategy to help the public and patients overcome barriers to healthy eating. However, they will likely also need to provide guidance through education and tools to overcome barriers related to meal planning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".