Culture and cognition in health systems change
Bibliographic record
Abstract
PURPOSE: Large-scale change involves modifying not only the structures and functions of multiple organizations, but also the mindsets and behaviours of diverse stakeholders. This paper focuses on the latter: the informal, less visible, and often neglected psychological and social factors implicated in change efforts. The purpose of this paper is to differentiate between the concepts of organizational culture and mental models, to argue for the value of applying a shared mental models (SMM) framework to large-scale change, and to suggest directions for future research. DESIGN/METHODOLOGY/APPROACH: The authors provide an overview of SMM theory and use it to explore the dynamic relationship between culture and cognition. The contributions and limitations of the theory to change efforts are also discussed. FINDINGS: Culture and cognition are complementary perspectives, providing insight into two different levels of the change process. SMM theory draws attention to important questions that add value to existing perspectives on large-scale change. The authors outline these questions for future research and argue that research and practice in this domain may be best served by focusing less on the potentially narrow goal of "achieving consensus" and more on identifying, understanding, and managing cognitive convergences and divergences as part of broader research and change management programmes. ORIGINALITY/VALUE: Drawing from both cultural and cognitive paradigms can provide researchers with a more complete picture of the processes by which coordinated action are achieved in complex change initiatives in the healthcare domain.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".