Doing the Dance of Culture Change: Complexity, Evidence and Leadership
Bibliographic record
Abstract
The challenge of culture change in hospitals must address three distinct but interwoven tensions: the need to shift paradigm and understand healthcare as a complex adaptive system; the challenge of knitting together the contributions of both evidence-based medicine and practice-based evidence; and the critical role of distributed, problem-focused leadership.The authors of the lead paper highlight five key issues in addressing this challenge: (1) the implementation of strategies like front-line ownership (FLO) in the context of macro-level social forces; (2) the central role of distributed leadership and its strengthening within the organization; (3) the need to attend to developing systems thinking skills at all levels; (4) the very significant challenge of how to scale up the labour-intensive change strategies within FLO, the role of "simple rules" and the potential for systems thinking tools such as concept mapping and dynamic modelling; and (5) the concurrent orchestration of not one culture change but three tensions in the challenge FLO represents to simpler versus complex adaptive systems, leadership and management and the balance between evidence-based medicine and practice-based evidence, at the clinical, organizational and macro-system levels.
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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 | 0.034 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.075 | 0.090 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".