Canadian general internal medicine residents’ perception of a pedagogical tool of online cases in obstetric medicine
Bibliographic record
Abstract
Background: Sufficient exposure to rarer medical problems around pregnancy is a challenge during short rotations in obstetric medicine (OM). A Canadian research group created online clinical cases, the CanCOM cases, to overcome this. Methods: We conducted an exploratory study to document the use and perceived utility of the CanCOM cases. 77 residents doing an OM rotation participated in our study. We used a survey to document their perception of CanCOM cases (12 items, 7-point scale), clinical exposure to several conditions (pre and post rotation; 41 items, 7-point scale) and use of the educational tool (1 item, 4-option scale). Results: CanCOM cases was perceived as an accessible and useful tool. Participants completed a median of 6/20 cases (range 1-20), and highly recommended the cases (6.48 ± 0.73 SD on a 7-point Likert scale). Conclusion: Despite some technical limitations, CanCOM cases was shown to contribute to clinical exposure to rare but essential medical conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.292 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".