A social network analysis of interactions about physical activity and nutrition among APPLE schools staff
Bibliographic record
Abstract
Comprehensive school health (CSH) is a holistic approach to school-based health promotion that involves active participation and buy-in of school community members, including school staff (e.g., support staff, teachers, school health champions, principals). Implementation and sustainability of CSH builds on complex relationships within the school that support school-level health promoting changes and understanding the social relationships that exist in a school setting is critical. Thus, the purpose of this study was to conduct a social network analysis to examine adviceseeking networks of staff within three schools involved with a CSH program called APPLE Schools (A Project Promoting healthy Living for Everyone in Schools) project approach. The degree to which school staff were central in the network (i.e., gave or sought physical activity or nutrition advice, were connected or disconnected to others; indegree/outdegree centrality and betweenness centrality) and the overall structure of the networks were assessed (i.e., optimal levels of density and centralization). School health champions and several other individuals in the network were shown to be key sources of physical activity or nutrition advice and were identified as central players in the network. Whole networks across schools had low density and betweenness centralization, with optimal levels of out-centralization, and low to optimal levels of incentralization. This research allowed us to gain an understanding of network structures and relationship patterns in CSH schools, with specific attention to the coordinating role of school health champions, and other central players within the network. These findings increase our understanding of advice relationships that exist in a school setting and how these relationships may support CSH implementation and sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".