Systematic review of the role of intercalated BScs in medical education
Bibliographic record
Abstract
Background and need for the project Intercalated BSc courses are (usually) optional extensions to UG medicine courses done by approx 1/3 of UK students. Such courses are also reported in Canada and Australia. There appears to be no agreed generic objectives for such courses, but by custom courses appear to explore an area of medicine in more depth, and usually involve research within the discipline. They are associated with deeper strategic learning styles. Aim: to review systematically published literature on the outcomes of students undertaking a BSc with the following outcomes: performance in final exams, impact on career choice and impact on professional skills and values. Methods A systematic review was undertaken to explore the outcome of BScs in medical education. Literature was searched using standard methods. We searched Medline/NLM, PsychLit, EMBASE and ERIC for papers that report student outcomes beyond the course outcome itself including performance in finals, subsequent careers, professional skills and values. Results We identified 7 papers1 that report student outcomes of which 6 show some improvement in student outcomes (finals performance, subsequent career progression). In terms of undergraduate performance two studies report outcomes ranging from no effect to an Odds Ratios (OR) of 4.4 (in favour of BSc students). No other data was retrieved on professional skills and values. For academic progression ORs are reported from 2.3 to 22, with one study reporting a difference of 0 to 42% in favour of BSc students. “Soft” literature such as editorials and opinion pieces support the role of iBScs in undergraduate medical education.
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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 | 0.018 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".