Floundering or Flourishing? Early Insights from the Inception of Integrated Care Systems in England
Bibliographic record
Abstract
Background: In 2022, England embarked on an ambitious and innovative re-organisation to produce an integrated health and care system with a greater focus on improving population health. This study aimed to understand how nascent ICSs are developing and to identify the key challenges and enablers to integration. Methods: Four ICSs participated in the study between November 2021 and May 2022. Semi-structured interviews with system leaders (n = 67) from health, social and voluntary care as well as representatives of local communities were held. A thematic framework approach supported by Leutz's five laws of integration framework was used to analyse the data. Results: The benefits of ICSs include enhancing the delivery of good quality care, improving population health and providing more person-centred care in the community. However, differences between health and social care such as accountability, organisational/professional cultures, risks of duplicating efforts, tensions over funding allocation, issues of data integration and struggles in engaging local communities threaten to hamper integration. Conclusions: Despite ICS's investing in the structural and relational components of integrated care, the unprecedented pressures on systems to reduce demand on primary and emergency care tackling elective backlogs may detract from a key goal of ICSs, improving population health and prevention.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".