<i>Evaluating a Food Bank</i>Recipe-Tasting Program
Bibliographic record
Abstract
Food banks mitigate immediate food insecurity, but their ability to promote healthy nutrition is constrained by how often recipients may visit and the range of foods available. In a descriptive study, a formative evaluation was completed of a combined heart-healthy recipe-tasting and education program that aims to promote healthy eating knowledge and skills in a group of food bank recipients in Hamilton, Ontario. Fifty-five adults were surveyed about food bank attendance, program awareness, perceived enhancement of knowledge and skills, and suggestions for program improvement. Most participants (73%) were positive about the program, and 91% wanted the program to continue. In addition, 78% would prepare the recipes sampled. In contrast, program awareness and planning food bank visits to coincide with the program were generally low. Food banks are potential sites for effective nutrition promotion programs. To reach more recipients, more frequent implementation and seeking the use of a designated room are suggested for the current program. The findings also suggest that the sampling approach to promoting healthy eating to food bank recipients deserves further study. For example, monitoring the selection of featured recipe ingredients would be a useful indicator of behaviour.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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 | 0.000 |
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".