A Cross-sectional Study Investigating Learning Approaches in Undergraduate Medical Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Objective The primary objective of this proof-of-concept cross-sectional study was to identify a framework for appraising the learning-approaches of undergraduate medical students in a competency based medical curriculum and correlating the results with teaching-approaches, as well as academic performance. The study was pursued at MBRU, which is a medical school in the Middle East with an undergraduate entry medical program. Results Our framework was blueprinted using the Approaches and Study Skills Inventory for Students (ASSIST) questionnaire, to which we made some modifications such that the overall cogency of the questionnaire wasn’t affected. Initial results with modified ASSIST at MBRU showed that most of our students adopted Deep or Strategic-learning approaches. This observation is in line with other studies in the literature, which shows that modified ASSIST is a suitable tool for mapping generic learning approaches with teaching approaches. Further, based on the insights from our initial results following the implementation of modified ASSIST, we have considered specific pedagogical strategies, in practice at MBRU, which cater to the generic learning approaches of majority of our undergraduate medical students. These pedagogical approaches, A. Feynman’s Technique; and B. Blended learning strategies, if implemented suitably in a curriculum will transform “Surface-learners” to “Deep/Strategic-learners”.
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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 | 0.122 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.023 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it