Bibliographic record
Abstract
PURPOSE: The goal of this study was to assess student perceptions of effective small group teaching during preclinical training in a medical school that promotes an integrated, systems-based undergraduate curriculum. In particular, students were asked to comment on small group goals, effective tutor behaviours, pedagogical materials and methods of evaluation. METHODS: Six focus groups were held with 46 Year 1 and 2 medical students to assess their perceptions of effective small group teaching in the 'Basis of Medicine' component of the undergraduate curriculum. Ethnographic content analysis guided the interpretation of the focus group data. RESULTS: Students identified tutor characteristics, a non-threatening group atmosphere, clinical relevance and integration, and pedagogical materials that encourage independent thinking and problem solving as the most important characteristics of effective small groups. Tutor characteristics included personal attributes and the ability to promote group interaction and problem solving. Small group teaching goals providing included opportunities to ask questions, to work as a team, and to learn to problem solve. CONCLUSION: This study highlighted the benefits of soliciting student impressions of effective small group teaching. The students' emphasis on group atmosphere and facilitation skills underscored the value of the tutor as a 'guide' to student learning. Similarly, their comments on effective cases emphasised the importance of clinical relevance, critical thinking and the integration of basic and clinical sciences. This study also suggested future avenues for research, such as a comparison of student and teacher perceptions of small group teaching as well as an analysis of perceptions of effective small group learning across the educational continuum, including undergraduate, postgraduate and continuing professional 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".