The impact of cognitive biases, mental models, and mindsets on leadership and change in the health system
Bibliographic record
Abstract
Understanding how cognitive biases, mental models, and mindsets impact leadership in health systems is essential. This article supports the notion of cognitive biases as flawed thinking or cognitive traps which negatively influence leadership. Mental models that do not fit with current evidence limit our ability to comprehend and respond to system issues. Resulting mindsets affect cognition, behaviour, and decision-making. Metacognition is critical. The wicked problems in today's complex health system require leaders and everyone involved to elevate their personal, organizational, and disciplinary perspectives to a systems level. Three examples of mental models/mindsets are reviewed. They do not change simply because we wish or will them to. The first step is being aware of what they are and how they impact our thinking and decision-making. Some tips for managing these traps are offered as examples of how to challenge our leadership approach in the health system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".