Exploring the consequences of combining medical students with and without a background in biomedical sciences
Bibliographic record
Abstract
CONTEXT: Medical schools have tended to admit students with strong backgrounds in the biomedical sciences. Previous studies have shown that those with backgrounds in the social sciences can be as successful in medical school as those with science backgrounds. However, the experience of being a 'non-science' student over time has not been well described. METHODS: A mixed-methods study was developed and run with the aim of elucidating the personal experiences of science and non-science students at our institution. Data were generated from a student survey that focused on participants' self-identification as science or non-science students, and on their sense of preparedness and stress, and from a series of student focus groups exploring participants' experiences of science and non-science issues in all aspects of their training. Descriptive statistics were generated for structured survey data. Focus group data and unstructured survey data were analysed to identify common themes. End-of-module and end-of-year examination data for the four class cohorts in the programme were also analysed to compare science and non-science student performance over time. RESULTS: There were clear differences between the experiences and performance of science and non-science students. We found dichotomies in students' self-reported sense of preparedness and stress levels, and marked differences in their examination performance, which diminished over time to converge around the third year of their studies. Combining science and non-science students in the same class affected the students to different extents and in different ways. The potential disruption of mixing science and non-science students diminished as their levels of performance converged. CONCLUSIONS: The psychosocial stress experienced by non-science students and the challenges it posed, in both their academic and their personal lives, have implications for how such students should be supported, and how curricula can be configured to afford quality learning for all medical students.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".