Understanding System-Level Intervention Points to Support School Food and Nutrition Policy Implementation in Nova Scotia, Canada
Bibliographic record
Abstract
Supporting the implementation of school food and nutrition policies (SFNPs) is an international priority to encourage healthier eating among children and youth. Such policies can improve equitable access, resources, and supports for healthy eating. However, despite the potential impact of SFNPs, several implementation barriers have been reported. This study sought to examine the system-level intervention points within a school food system using a complex systems framework. We conducted semi-structured interviews with various stakeholders working to influence the school food system in Nova Scotia, Canada. We sought to understand their roles and experiences with the SFNP by applying the Intervention Level Framework (ILF), a novel, solutions-oriented approach to better understand how complex systems function. Participants (n = 33) included teachers, parents, cafeteria workers, public health staff and non-profit organizations. Interview transcripts were first coded, then themed and finally analyzed using the ILF, resulting in three intervention points within the school food system. These were defined as: (1) Actors and Elements, (2) System Regulation and Interconnections and (3) Purpose and Values. We concluded that understanding the interactions between these system levels and stakeholder roles can help to inform the development of relevant policy strategies that better support healthier school food environments in this jurisdiction.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".