Strengthening the evidence and action on multi-sectoral partnerships in public health: an action research initiative
Bibliographic record
Abstract
INTRODUCTION: The Public Health Agency of Canada's Multi-sectoral Partnerships Initiative, administered by the Centre for Chronic Disease Prevention (CCDP), brings together diverse partners to design, implement and advance innovative approaches for improving population health. This article describes the development and initial priorities of an action research project (a learning and improvement strategy) that aims to facilitate continuous improvement of the CCDP's partnership initiative and contribute to the evidence on multi-sectoral partnerships. METHODS: The learning and improvement strategy for the CCDP's multi-sectoral partnership initiative was informed by (1) consultations with CCDP staff and senior management, and (2) a review of conceptual frameworks to do with multi-sectoral partnerships. Consultations explored the development of the multi-sectoral initiative, barriers and facilitators to success, and markers of effectiveness. Published and grey literature was reviewed using a systematic search strategy with findings synthesized using a narrative approach. RESULTS: Consultations and the review highlighted the importance of understanding partnership impacts, developing a shared vision, implementing a shared measurement system and creating opportunities for knowledge exchange. With that in mind, we propose a six-component learning and improvement strategy that involves (1) prioritizing learning needs, (2) mapping needs to evidence, (3) using relevant data collection methods, (4) analyzing and synthesizing data, (5) feeding data back to CCDP staff and teams and (6) taking action. Initial learning needs include investigating partnership reach and the unanticipated effects of multi-sectoral partnerships for individuals, groups, organizations or communities. CONCLUSION: While the CCDP is the primary audience for the learning and improvement strategy, it may prove useful for a range of audiences, including other government departments and external organizations interested in capturing and sharing new knowledge generated from multi-sectoral partnerships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.373 | 0.263 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.028 | 0.026 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".