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 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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".