Opening the doors for school health—An exploration of public health nurses’ capacities to engage in comprehensive school health programs
Bibliographic record
Abstract
OBJECTIVES: Public health nurses (PHNs) have a significant role in engaging the voice and actions of school communities in promoting the health of children and youth. School nursing was one of the early 20th century public health nursing foci and specialties in Canada, however over several decades, there has been a gap in actualizing PHNs' full potential in schools. At the same time, intersectoral and interdisciplinary comprehensive school health (CSH) models have emerged as exemplars of partnerships between schools and communities to advance health promotion and ultimately chronic disease prevention with school populations (Pan-Canadian Joint Consortium for School Health, ; World Health Organization, ). DESIGN AND MEASUREMENT: Using a participatory action research methodology we explored the role of PHNs in CSH, drawing on the concept of engagement in intersectoral healthy school teams. RESULTS AND CONCLUSIONS: The three themes that emerged from the data analysis were: facilitators of public health nursing engagement, barriers to public health nursing engagement, and the influences of community context on engagement. Overall, findings indicate that the PHN role in CSH must be developed and supported so that PHNs remain a vital link between school health communities, programs, and policies in the promotion of health.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".