Collaborative Leadership in Integrated Care Systems; Creating Leadership for the Common Good
Bibliographic record
Abstract
The COVID-19 pandemic has become a catalyst for change, but such change can only happen through collaborative leadership which maintains a focus on relationships and purpose rather than solely on outputs or outcomes.This conceptual article explores how health and social care integration has been offered as one potential solution to the challenge of health and social care transformation.Specifically, Integrated Care Systems in England are intended to provide regional governance, to provide public services in a coherent and robust way.We explore this development in relation to three key aspects: the macro-level global policy context; the meso-level organizational behaviour and culture; and the micro-level practice of individual leaders and managers.It is found that, whilst the organizational structure of Integrated Care Systems offers great promise, collaborative leadership is critical to realize truly resilient and sustainable collaborative relationships. MAD statementIntegrated Care Systems have been developed at the system level with little consideration of the leadership that will be required to implement collaborative action across health and social care.Coming out of the COVID-19 crisis there is an opportunity to create leadership for the common goodbut this will require energy, purpose, and courage across all levels of the governance system.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 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".