Bibliographic record
Abstract
It is often debated if medical electives are beneficial for students. While medical electives are not mandatory for students in the developing world, they are an important part of medical training in some Western universities (UCSD School of Medicine, n.d.) and have been part of UK undergraduate training since the 1970s (Cruikshank & Walsh, 1980). In the West medical schools form committees to guide, counsel, and help students plan their electives during vacations. In South Asia, the concept of electives is minimally encouraged; however, the students themselves share their elective experience and encourage other students to take electives, mostly through online forums. Electives often provide students a chance to work in a different setup, with different disease prevalence patterns, hospital management protocols, and learning experiences under various doctors with diverse problem solving approaches. It is also a two pronged tool whereby students can enhance their clinical skills and find opportunities to obtain a research project under the mentorship of research oriented academic consultants. This article is a brief sketch of experiences encountered by a South Asian medical student on a clerkship elective rotation in cardiology at a tertiary care hospital in Canada.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".