“Grabbing” Autonomy When the Learning Environment Doesn’t Support it: An Evidence-based Guide for Medical Learners
Bibliographic record
Abstract
According to self-determination theory (SDT), environments which support the basic psychological needs for autonomy, competence, and relatedness will facilitate autonomous motivation, learning, and wellness. On the other hand, environments which introduce external controls and power dynamics into the equation will do the opposite. Educational studies support these principles, yet most have focused on learners' need satisfaction as a passive process (e.g., via support or hindrance by educators), rather than the agentic pursuit that SDT emphasizes. In this commentary, I draw on my experience as a practicing physician and SDT researcher, and focus on how medical learners can "grab" more autonomy when the learning environment does not support it. I present a hypothetical case of a preceptor whose teaching style is controlling and unfortunately well-known to medical learners. I then unpack the case and outline different strategies that medical learners can use to navigate this type of interpersonal conflict.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".