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Bibliographic record
Abstract
Introduction: In 2010, Dalhousie University implemented a new MD curriculum, placing an emphasis on self-directed learning (SDL) time. This study sought to understand how students use this time and whether they would benefit from more structure during SDL. We hypothesized that students spend significant amounts of SDL time on non- academic activities and would prefer to have more specific guidance and tasks. Methods: Pre-clerkship medical students at Dalhousie (n=223) were sent an online survey consisting of 18 questions using a combination of Likert scales, and text boxes for qualitative responses. Chi-square analysis was performed for each survey question. Results: Eighty-five percent (n=93) of medical students responded that time scheduled for SDL was sufficient (p<0.001) and 67% (n=73) responded that they would benefit from more specific guidance and tasks during SDL time (p<0.001). Forty-five percent responded that they “rarely” spent SDL time on non-academic activities (n=49), however only 14% (n=15) responded “most of the time” (p<0.001). Conclusion: The majority of respondents used SDL time for academic activities but felt they would benefit from more specific guidance and tasks. This is inconsistent with our hypothesis that students are spending significant amounts of SDL time on non-academic activities, but supports our hypothesis that students would prefer more structure.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.757 | 0.611 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".