Teaching a Man to Fish: An Evaluation of a Chronic Disease Management Men’s Cooking Class
Bibliographic record
Abstract
There is limited data on the effects of cooking classes on male participants. The LiveWell Chronic Disease Management program’s Men’s Cooking Class (MCC) aims to help participants gain skills and confidence with food to manage chronic diseases more independently and improve their health. This paper evaluates whether, and how, the program is effective in achieving its goals. A qualitative process was used to collect data from past program participants. Data collection included telephone interviews conducted with a sample of 27 past MCC attendees and a focus group held with a subsample of seven participants. Thematic analysis was performed on collected data. Five major themes emerged, including (i) practical and applicable content, (ii) kinesthetic teaching and learning, (iii) catering to the interests of participants, (iv) tailoring to the demographic, and (v) enjoyment and engagement. Findings indicate the current LiveWell MCC program is effective in meeting its goals. The themes identified are aspects of the program that contribute to this effectiveness. The thematic findings indicate areas in which to continuously adapt and monitor the effectiveness of this program and serve as recommendations for other programming. Further research on the long-term impact of MCC for self-management of chronic disease is needed.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".