It’s just the right thing to do: Conceptualizing a theory of change for a school food and beverage sales environment interv ention and implications for implementation evaluation
Bibliographic record
Abstract
School food environments are the target of nutrition interventions and evaluations across the globe. Yet little work to-date has articulated the importance of developing a theory of change upon which to base evaluation of both implementation and outcomes. This paper undertakes an interpretive approach to develop a retrospective theory of change for an implementation evaluation of British Columbia's school food and beverage sales Guidelines. This study contributes broadly to a nuanced conceptualization of this type of public health intervention and provides a methodological contribution on how to develop a retrospective theory of change with implications for effective evaluation. Data collection strategies included document analysis, semi-structured interviews with key stakeholders, and participant observation. Developing the logic model revealed that, despite the broad population health aims of the intervention, the main focus of implementation is to change behaviors of adults who create school food environments. Derived from the analysis and interpretation of the data, the emergent program theory focuses on the assumption that if adults are responsibilized through information and education campaigns and provided implementation tools, they will be 'convinced' to implement changes to school food environments to foster broader public health goals. These findings highlight the importance of assessing individual-level implementation indicators as well as the more often evaluated measures of food and beverage availability.
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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 | 0.039 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.084 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".