Examining Cross-sector Collaboration to Support Implementation of the Ontario Public Health Standards – School Health Standard Guideline
Bibliographic record
Abstract
In Canada and beyond, schools have been an ideal setting to promote the health of children and youth, and the Comprehensive School Health approach has been an evidence-based way to engage in this work with a demonstrated return on investment. Ontario has established commitment to the Comprehensive School Health approach through provincial level policy, such as the Ontario Public Health Standards (OPHS). Through the OPHS School Health Standard, Public Health Units (PHUs) in Ontario are directed to work in partnership and collaboration with school boards and schools to develop a Comprehensive School Health promotion approach to promote the health of children and youth. Literature has identified that collaboration between the health and education sectors is an important facilitator of the Comprehensive School Health approach and related policy direction. Through a qualitative descriptive study this thesis project sought to examine the structures, practices, mechanisms, as well as facilitators and barriers to partnership and collaboration between Ontario PHUs and school boards. The Health in All Policies (HiAP) model was used to guide data collection and analysis. Data were collected from 13 PHUs through an electronic survey and document collection. Findings identified different provincial level policies that facilitate collaboration between PHUs and school boards (e.g. OPHS, PPMs); however, a barrier noted was the lack of one consistent or overarching provincial policy across the health and education sectors that supports or facilitates collaboration. At the local level, PHUs and school boards declare and demonstrate their partnership commitments in a variety of ways, from formal practices such as the development of partnership declarations and committees, to informal practices such as verbal agreements and ad hoc consultations. PHUs dedicate resources to collaboration with school boards which is dependent on PHU funding and capacity. A critical resource identified was human resources, with the School Health Manager and Public Health Nurses identified as two important roles that champion and facilitate collaborative practices with school boards and schools. To monitor and evaluate the effectiveness of partnership and collaboration, PHUs and school boards engage in a variety of informal and formal practices, such as the provision of ongoing feedback, to monitoring collective key performance indicators.
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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 | 0.070 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".