Rhetoric Versus Reality – Embedding a New Relationship Within Integrated Care Systems for Third Sector Organisations
Bibliographic record
Abstract
Introduction: Within the UK, NHS England has outlined the integral role of third sector organisations as a strategic partner in integrated care systems. This study sought to explore the embedding of a 'new relationship' in the co-design and delivery of 'local' services. Methods: Thirteen semi-structured interviews were conducted within a local authority area in England, with leaders from both the statutory and third sector. Interviews were analysed using framework analysis. Findings and Discussion: Findings suggest there is a need to go beyond the rhetoric in embedding a 'new relationship' with the third sector. More needs to be done to change the narrative as to how the third sector is perceived, for sectoral stereotypes to be dispelled, to move beyond tokenistic engagement and focus on how improving health can be tackled together. Whilst place-based forms of governance will differ, a greater understanding by the statutory sector of 'local' organisational and individual dynamics, capabilities and perspectives is paramount. Conclusion: The study concludes that policy narratives are not underpinned with institutional structures and mechanisms. Without a concerted effort and commitment to meaningful engagement, there is a risk that third sector goodwill dissipates in the face of the latest iteration of policy rhetoric.
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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 | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".