Towards a better understanding of medical students’ mentorship needs: a self-determination theory perspective
Bibliographic record
Abstract
INTRODUCTION: Mentorship programs are ubiquitous in medical education. However, few emphasize equal development for learners and mentors, or incorporate clinical skills, which may be important for promoting medical students' self-determination. Central to this consideration are the three basic psychological needs for autonomy, competence, and relatedness, described by Self-Determination Theory (SDT). Grounded in SDT, this study assesses the extent that meeting these needs, in a near-peer mentoring program, impacts learners' and mentors' motivation and perceived competence about learning and teaching of clinical knowledge, respectively. METHODS: Medical students from the University of Saskatchewan, who participated in its near-peer mentoring program (PULSE: Peers United in Leadership & Skills Enhancement), were invited to complete an anonymous survey. Regression was used to determine how the program's learning climate impacted learners' and mentors' psychological need satisfaction and perceived competence within their mentorship role. RESULTS: Learners and mentors both rated PULSE as highly needs-satisfying. In turn, this was associated with greater perceived competence about learning and teaching of the material. CONCLUSIONS: Findings from this study suggest that mentoring programs in medical education, which support learners' basic psychological needs, may promote their motivation and perceived competence-both about learning and also teaching of clinical skills. The implications of these results are discussed from an SDT perspective, with respect to mentoring programs in medical education.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".