From Suppression to Integration: A Self‐Determination Theory Perspective on Emotion Regulation in Medical Education
Bibliographic record
Abstract
PURPOSE: Emotions influence physicians' learning, well-being and clinical care. Yet, emotion regulation remains an underdeveloped area in medical education, often lacking the conceptual clarity and structure needed for consistent teaching and assessment. This paper introduces self-determination theory (SDT) as a comprehensive framework for advancing emotion regulation in clinical training and practice. ARGUMENT: SDT describes three distinct forms of emotion regulation-dysregulation, suppression and integration-each shaped by the degree to which basic psychological needs for autonomy, competence and relatedness are supported. Clinical learning environments may unintentionally foster suppression or dysregulation through cultural norms and structural pressures. These patterns can undermine physician wellness and patient care, whereas integrated regulation has been linked to resilience, empathy and sustained professional engagement. INSIGHTS: SDT brings conceptual precision, strong empirical foundations and existing validated tools for assessment. It offers educators and institutions a ready-to-use framework to support emotional development through curriculum design, coaching and organisational culture, enabling emotion regulation to be approached as a developmental skill. CONCLUSION: Applying SDT to emotion regulation offers a promising path forward for medical education. It provides a shared language and actionable strategies to foster emotional integration, enhancing learner development, care quality and system sustainability.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".