Unpacking the Theory Behind One Health Food Safety Programs: A Vietnam Case Study
Bibliographic record
Abstract
Many One Health programs are inherently complex, characterized by multiple perspectives from multiple sectors, delivery across various scales, and a focus on complex problems at the convergence of people, animals, and the environment. This complexity makes them difficult to conceptualize, requiring frameworks to organize the different program components. Evaluation frameworks that unpack the sequence of events linking program activities to outcomes (e.g., Theory of Change) and track outcomes (e.g., Outcome Mapping) show promise in supporting the development of One Health programs. While widely used in international development and health contexts, there has been little reflection on the use of Theory of Change and Outcome Mapping within One Health efforts. This paper reflects on the process of applying these frameworks to conceptualize a One Health food safety program in Vietnam. We find Theory of Change fostered the characterization of a change pathway toward safer pork, while Outcome Mapping kept us informed of where along the change pathway we were. One Health programs considering evaluation frameworks should adopt elements that make sense to them, be intentional about co-designing the evaluation, and view evaluation as a process, not a product.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".