Expanding our conceptualization of program implementation: lessons from the genealogy of a school-based nutrition program
Bibliographic record
Abstract
This work presents a theoretical framework in which health promotion and health education program implementation can be conceived as an open dynamic system. By tracing the evolution of an elementary school-based nutrition program from its conception to its recent form, we construct a program genealogy. Data were derived from two interviews and three historical documents from which historical events were identified and reconstructed in the form of a tree analogy. Data analysis ensued using concepts from the actor-network theory about social innovation. These concepts identified social and technical program attributes and situated them within a process which evolved over time, thus permitting the program's genealogy to appear. The genealogy was found to be influenced by the ways in which the involved actors interpreted the issue of food security, namely, as a professional issue, with a nutrition education response and as a social issue, with a community-building response. The interaction between the interests of the actors and the technical components of the program resulted in three temporal program iterations. The results highlight the important role played by the involved actors during program implementation and suggest the need to take these interests into consideration during all phases of program planning.
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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".