Social studying and learning among medical students: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Medical students study in social groups, which influence their learning, but few studies have investigated the characteristics of study groups and the impacts they have on students' learning. A scoping review was conducted on the topic of informal social studying and learning within medical education with the aim of appraising what is known regarding medical student attitudes to group study, the impact of group study on participants, and the methods that have been employed to study this. METHODS: Using Arksey and O'Malley's scoping review principles, MEDLINE, EMBASE and CINAHL were searched, along with hand-searching and a targeted search of the grey literature; 18 peer reviewed and 17 grey literature records were included. RESULTS: Thematic conceptual analysis identified a number of themes, including: the nature of group study; the utility and value of group studying including social learning facilitating student engagement, social learning as a source of motivation and accountability, and social learning as a source of wellbeing; and student preferences related to group studying, including its homophilic nature, transgressiveness, and effectiveness. Despite these emerging factors, the evidence base for this phenomenon is small. DISCUSSION: The findings in this scoping review demonstrate a clear role for social interaction outside of the classroom, and encourage us to consider the factors in student networking, and the implications of this on medical students' academics. We also highlight areas in need of future research to allow us to better situate informal social learning within medical education and to enable educators to support this phenomenon.
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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 | 0.017 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.026 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".