Health inequalities tackled through intersectoral collaboration: longitudinal process issues and insights
Bibliographic record
Abstract
This study contributes to ongoing reflections and debate on the legacy of the Ottawa Charter by illustrating how contemporary forms of intersectoral collaboration can be mobilized to address persistent health inequalities. Collaborations involving organizations from diverse sectors are often viewed as well-positioned to tackle complex health challenges, yet they frequently encounter political, organizational and cultural barriers that hinder their effectiveness. This paper uses a longitudinal approach to explore issues in relation to the formation and sustainability of a multi-sector collaboration in one geographic area in the UK, working under the banner of the Health Determinants Research Collaboration (HDRC)-a programme which seeks to further understand health determinants and to improve health outcomes in communities. Through qualitative interviews at two time points-12 months apart-with constituents of the collaboration, the data demonstrated a clear and shared vision for the collaboration and a neat 'dovetailing' of skill-sets related to community brokerage; academic rigour; and statutory legitimacy. While the collaboration under focus here was in its infancy, cultural, and practical tensions in ways of working; trust issues; pace of working; and philosophy were predicted to, and indeed did, emerge and required careful monitoring to ensure intended outcomes were not derailed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".