Changes in Study Strategies of Medical Students between Basic Science Courses and Clerkships Are Associated with Performance
Bibliographic record
Abstract
We tested the hypothesis that medical students change their study strategies when transitioning from basicscience courses to clerkships, and that their study practices are associated with performance scores. Factor scoresfor three approaches to studying (construction, rote, and review) generated from student (n=150) responses to aquestionnaire were correlated to examination and clinical performance scores. Composite factor scores werecompared using a paired t-test and sign test to examine changes in study practices as students transitioned frombasic science courses to clerkships. The construction approach to studying was more likely to have a positive andstronger relationship to examination scores in both courses and clerkships, but showed no significantassociations with clinical performance scores. Our analyses indicated that students are more likely to increasetheir use of study practices associated with construction of knowledge as they transition from courses toclerkships. Although learning is a complex endeavor, students employing construction study strategies are morelikely to outperform their peers who rely mostly on rote and review practices. Transitioning from basic sciencecourses to the clerkships students tend to utilize more construction study practices suggesting that students areresponsive to their learning environments when selecting study strategies.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".