Advance Quantity Meal Preparation Pilot Program Improves Home-Cooked Meal Consumption, Cooking Attitudes, and Self-Efficacy
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of a group-based Advance Quantity Meal Preparation (AQMP) program on the consumption of home-cooked meals, cooking attitudes, and self-efficacy in healthy adults. METHODS: Participants (n = 10) in a group setting prepared healthy meals weekly consisting of 10 entrees and 5 snacks for 6 weeks. A survey assessing cooking attitudes, cooking self-efficacy, and cooking behavior and consumption at 3 time points: preprogram, postprogram (T2), and 3 months postprogram (T3). RESULTS: The AQMP program increased the proportion of overall home-cooked meal consumption (T2, P = 0.03), home-cooked dinner consumption (T2, P = 0.04), cooking attitudes (T3, P = 0.01), and cooking self-efficacy (T2, P = 0.002). CONCLUSIONS AND IMPLICATIONS: This pilot study indicates that AQMP may increase home-cooked meal consumption, cooking attitudes, and cooking self-efficacy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".