Characteristics of Current Teaching Kitchens: Findings from Recent Surveys of the Teaching Kitchen Collaborative
Bibliographic record
Abstract
Teaching kitchens are physical and virtual forums that foster practical life skills through participation in experiential education. Given the well-supported connection between healthy eating patterns and the prevention and management of chronic diseases, both private and public organizations are building teaching kitchens (TKs) to enhance the health and wellness of patients, staff, youth, and the general community. Although implementation of TKs is becoming more common, best practices for starting and operating programs are limited. The present study aims to describe key components and professionals required for TK operations. Surveys were administered to Teaching Kitchen Collaborative (TKC) members and questions reflected seven primary areas of inquiry: (1) TK setting(s), (2) audiences served, (3) TK model(s), (4) key lines of operations, (5) team member who manages or directs the TK, (6) team member(s) who performs key operations and other professionals or partnerships that may be needed, and (7) the primary funding source(s) to build and operate the TK (among various other topics). Findings were used to articulate recommendations for organizations seeking to establish a successful TK as well as for TKs to expand their collective reach, research capacity, and impact.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".