Intrinsic motivation of preclinical medical students participating in high-fidelity mannequin simulation
Bibliographic record
Abstract
Introduction: While medical schools strive to foster students' lifelong learning, motivational theories have not played an explicit role in curricular design. Self-determination Theory is a prominent motivational theory. It posits that perceived autonomy, competence and relatedness foster intrinsic motivation. This study explores the effects of autonomy on intrinsic motivation in medical students participating in high-fidelity mannequin simulation. Methods: A non-randomised crossover trial compared first-year medical students participating in (1) required simulation sessions with predetermined learning objectives and (2) extracurricular simulation sessions with student-directed learning objectives. An adapted Intrinsic Motivation Inventory (IMI) was used to assess intrinsic motivation, perceived autonomy, competence and relatedness. Each participant completed the IMI survey after each type of session. Variables were compared with signed-rank tests. Results: All 22 participants completed the IMI after both types of session. Perceived autonomy was significantly higher during extracurricular simulation (p<0.001), but intrinsic motivation, competence and relatedness were not. Intrinsic motivation correlated with autonomy (RS=0.57 and extracurricular simulation, ES=0.52), competence (RS=0.46 and ES=0.15) and relatedness (RS=0.51 and ES=0.64). The IMI subscales had good internal consistency (Cronbach's α=0.84, 0.90, 0.90 and 0.76 for intrinsic motivation, autonomy, competence and relatedness, respectively). Conclusions: Extracurricular sessions increased students' perceived autonomy, but they were highly intrinsically motivated in both settings. Further study is needed to understand the relationship between perceived autonomy and intrinsic motivation in medical education learning activities. The IMI shows promise as a measurement tool for this work.
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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".