Promoting the Learning of Basic Sciences in a Changing Small-Group Learning Landscape. Interviews of tutors and students in a medical program
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Tomorrow's medical graduate needs to be equipped today to be able to make sense of continued advancements in medical science and then apply these to the benefit of patents and communities. It is important that current methods of teaching and learning in medical programs such as problem-based learning are subject to critical evaluation as to whether they are fit for purpose, and many medical programs are questioning whether problem-based learning provides adequate grounding in basic science. In this context, we interviewed twelve tutors and four students of one distributed medical program in order to explore their views on what elements of PBL promoted understanding of basic science. Our participants reported that setting a clear agenda about basic science learning was crucial; that tutor preparation and guides needed to take account of different tutor backgrounds; particular styles of prompting and questioning from the tutor were valuable; and that students could benefit from being primed with key introductory concepts about how basic sciences related to each other prior to commencing PBL. These findings will be useful for medical curriculum developers who are seeking to innovate with their curriculum but wish to retain what may be currently most valuable by stakeholders.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".