<i>Evaluation of a Collective Kitchens Program</i> Using the Population Health Promotion
Bibliographic record
Abstract
To evaluate the impact of the Calgary Health Region Collective Kitchen Program on various Population Health Promotion Model health determinants, data were collected through mail-in questionnaires that examined the members' (n=331) and coordinators' (n=58) perspectives of the program. Seventy-nine members (24%) and 26 coordinators (45%) were included in the study. Three incomplete questionnaires (from prenatal program members) were discarded. Sixty-one percent of members who reported income level and family size (n=61) had incomes below the low-income cut-off. Fifty-eight members (73%) reported improvements in their lives because of the program. Sixty-four members (81%) perceived they learned to feed their families healthier foods. The members reported their fruit and vegetable consumption before and since joining a collective kitchen, and the proportion of those consuming at least five fruit and vegetable servings a day rose from 29% to 47%. The most common reasons for joining this program concerned social interactions and support. Over 90% of the coordinators perceived that they were competent to coordinate a kitchen. The results indicate that the collective kitchens program addresses several health determinants, and may increase members' capacity to attain food security and to achieve improved nutritional health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".