Collaborative Intersectoral Approaches to Nutrition in a Community on the Urban Fringe
Bibliographic record
Abstract
A case study is presented that describes the 10-year evolution of a local intersectoral project aimed at improving components of a community's food system as an approach to improving nutrition. Aspects of innovation and good contemporary practice in collaborating for health promotion are illustrated. Key initiators of the project were a university public health department, a community health service, and a local government authority. Players brought into the process included the agricultural sector and food retailers. Several strategies have contributed to the success and institutionalization of the project. These include a specific focus on organizational development and capacity building among the key intersectoral partners and the use of formative evaluation methods to hasten the natural phases of collaborative problem solving. The project achieved many policy- and system-level changes. The impact on food consumption patterns is still to be evaluated.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.041 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".