Integrating fixed norms and relational responsiveness in medical education: Redefining autonomy
Bibliographic record
Abstract
In this short communication, we reconceptualize autonomy in medical education, particularly within the context of professional identity formation (PIF). Traditionally, autonomy has been framed as independence and achievement based on 'fixed norms'. However, identity is also shaped through 'relational responsiveness', which emphasizes mutual engagement and co-construction. Drawing on Asian cultural understandings of selfhood, we propose viewing autonomy as integrity enacted within a network of mutual responsibility. We argue that both fixed norms and relational responsiveness are essential, and we introduce a preliminary framework that highlights their complementary roles. While responsiveness fosters adaptability and connectedness, it can lead to identity diffusion if not grounded in internal standards. A blended model of both modes offers a more inclusive, culturally sensitive, and resilient approach to PIF in today's complex medical landscape.
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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 | 0.022 |
| 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) | 0.005 | 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".