Ecological theories of systems and contextual change in medical education
Bibliographic record
Abstract
INTRODUCTION: Contemporary medical practice is subject to many kinds of change, to which both individuals and systems have to respond and adapt. Many medical education programmes have their learners rotating through different training contexts, which means that they too must learn to adapt to contextual change. Contextual change presents many challenges to medical education scholars and practitioners, not least because of a somewhat fractured and contested theoretical basis for responding to these challenges. There is a need for robust concepts to articulate and connect the various debates on contextual change in medical education. Ecological theories of systems encompass a range of concepts of how and why systems change and how and why they respond to change. The use of these concepts has the potential to help medical education scholars explore the nature of change and understand the role it plays in affording as well as limiting teaching and learning. METHODS: This paper, aimed at health professional education scholars and policy makers, explores a number of key concepts from ecological theories of systems to present a comprehensive model of contextual change in medical education to inform theory and practice in all areas of medical education. RESULTS: The paper considers a range of concepts drawn from ecological theories of systems, including biotic and abiotic factors, panarchy, attractors and repellers, basins of attraction, homeostasis, resilience, adaptability, transformability and hysteresis. Each concept is grounded in practical examples from medical education. CONCLUSION: Ecological theories of systems consider change and response in terms of adaptive cycles functioning at different scales and speeds. This can afford opportunities for systematic consideration of responses to contextual change in medical education, which in turn can inform the design of education programmes, activities, evaluations, assessments and research that accommodates the dynamics and consequences of contextual change.
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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 | 0.002 | 0.032 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".