Effect of an Undergraduate Medical Curriculum on Students??? Self-Directed Learning
Bibliographic record
Abstract
PURPOSE: Lifelong, self-directed learning (SDL) has been identified as an important ability for medical graduates. To evaluate the effect of the University of Toronto Faculty of Medicine's revised undergraduate medical curriculum on students' SDL, a cross-sectional study was conducted. METHOD: A questionnaire package was mailed to 280 randomly selected students, 70 from each of the four years of the curriculum. The package contained the two most widely recognized, extensively used, and validated instruments of SDL (Guglielmino's 58-item Self-Directed Learning Readiness Scale and Oddi's 24-item Continuous Learning Inventory) and Ryan's two-part Self-Assessment Questionnaire. An identification number and sociodemographic questions were included with the questionnaires. Data analysis was completed using chi-square for differences of proportions, analysis of variance for differences between means, and linear regression for trends. RESULTS: A total of 250 (89.3%) complete questionnaire packages were returned. No significant trend in SDL was evident by curriculum year, and similar SDL levels were observed for women and men. However, a significant positive trend in SDL was found with the highest level of premedical education achieved (undergraduate only, masters, or doctoral). Further, students' perceptions concerning the importance of SDL decreased according to year in the curriculum. CONCLUSION: This study found no evidence that students' self-reported SDL is positively influenced by the current undergraduate medical curriculum at the University of Toronto Faculty of Medicine.
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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 | 0.008 |
| 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) | 0.005 | 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".