Medical student curiosity and medical education: A descriptive study
Bibliographic record
Abstract
It has been suggested that intellectual curiosity is important to the practice of medicine and to medical student learning. It has also been proposed that medical education may suppress rather than promote the curiosity of medical learners. Despite these claims, curiosity has not been empirically investigated in medical education. The purpose of this study was to explore the concept of curiosity, as it is manifested in medical students across undergraduate medical education. A cross-sectional design was employed in which medical students, in all four years of undergraduate training, completed the Melbourne Curiosity Inventory (MCI) as a measure of their state-trait epistemic (i.e., intellectual) curiosity. The psychometric properties of the MCI were monitored to ensure validity of use in this study context. State and trait epistemic curiosity were described, and a repeated measures ANOVA was used to compare students across year of training. The state-trait curiosity construct was found to be valid in this study context, and was reliably measured by the MCI. Four hundred and two out of 751 students completed the inventory (53.5%). Trait curiosity was significantly higher than state curiosity overall (M = 64.5, SD = 8.5 vs. M = 58.5, SD = 11.6, respectively) and within each year of training. Moreover, state curiosity was found to be particularly low in the sample of year-2 students. This study is the first to empirically investigate intellectual curiosity in medical education. The findings suggest the possibility that medical education may be suppressing rather than promoting the curiosity of medical students.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".