Impacts of A Taste of African Heritage: A Culinary Heritage Cooking Course
Bibliographic record
Abstract
OBJECTIVE: To examine changes in cooking and dietary behaviors and health outcomes following participation in A Taste of African Heritage, a culinary heritage cooking course. DESIGN: One group, pre-post program design, surveys, and physical assessments. SETTING: Community centers, churches, health care settings, homes, and housing complexes. PARTICIPANTS: Participants (n = 586, 84% women) were recruited by partner organizations from 2013-2018 across 21 states and the District of Columbia. INTERVENTION(S): A 6-week cooking curriculum that teaches history, nutrition, and cooking techniques to reconnect participants with the vibrant, healthy traditions of the African Diaspora. MAIN OUTCOME MEASURE(S): Weekly frequency of cooking, food group intake, and exercise assessed by surveys, measured weight, waist circumference, and blood pressure. ANALYSIS: Linear and logistic mixed-effects models with random intercepts for participant and teacher, sex and site type as covariates comparing pre-post physical measurements and binary behavioral outcomes, P < 0.0033 for statistical significance after Bonferroni correction. RESULTS: Intake frequencies of fruit, vegetable, and greens and exercise frequency were improved from preprogram to postprogram (all P < 0.0021). Weight, waist circumference, and systolic blood pressure were also improved from preprogram to postprogram (all P <0.0001). CONCLUSIONS AND IMPLICATIONS: Participation in A Taste of African Heritage was associated with positive behavior changes and health outcomes. Integrating cultural heritage and behaviors are positive components to connect participants to healthy old ways or traditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 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) | 0.015 | 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".