Putting Self‐Determination Theory Into Practice: A Practical Tool for Supporting Medical Learners’ Motivation
Bibliographic record
Abstract
Self-determination theory (SDT) is a well-established framework that identifies three basic psychological needs-autonomy, competence and relatedness-as essential for motivation, engagement and well-being. Despite increasing recognition of SDT's relevance in medical education, educators lack practical tools to translate theory into daily teaching practice. This paper addresses that gap by offering a concise, evidence-informed table of actionable strategies for educators to support learners' psychological needs in routine interactions. Targeted at clinical teachers and program leaders, the tool is designed to guide real-time application of SDT principles, fostering learning environments where motivation and thriving can take root. A key feature of the tool is its inclusion of specific, example language that educators can use to support autonomy, competence and relatedness in everyday clinical interactions. In addition, I present a single-page visual summary (Figure 1) that brings together the highest-yield SDT strategies in a concise, accessible reference. This diagram serves as a practical checkpoint and reminder for educators to align their daily interactions with SDT principles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".