A Health Systems Policy Framework on “How to” Build Cross-Sector Collaboration: Perspectives From Health Administrators and Leaders
Bibliographic record
Abstract
There are many barriers/challenges bringing multiple stakeholders within health and non-health together to collaborate to address population health. This study aims to identify the key components to build successful cross-sector collaboration and develop a policy framework for health systems integration and transformation. We conducted quantitative surveys and qualitative interviews with health administrators and leaders who volunteered to participate on six newly established teams or “Tables” to improve population health locally in Ontario, Canada. Using thematic analysis and methodological triangulation, we identified emergent themes that were confirmed by member checking. The Relational Coordination survey response rate was 62% (n=45). The survey results were correlated with the twelve interviews and member checking. Drawing from the perspectives of the health administrators and leaders of the “Tables”, the emergent themes identified for successful cross-sector collaboration were: 1) systems change mindset, 2) inter-dependency, 3) inter-organizational relationships, and 4) self-organizing capacity. A health systems policy framework on “how to” build cross-sector collaboration was developed to support and achieve health systems integration.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".