Relationship Between Small Group Problem‐Solving Activity and Lectures in Health Science Curricula
Bibliographic record
Abstract
Components of problem-based education, such as small group teaching, are being implemented in diverse health curricula. Implementation, however, is often motivated by the intuitive appeal of many problem-based learning components, when what is needed is the detailed examination of how these components support students' integration of knowledge as well as continuity of their learning experiences. This study presents an investigation of the relationship between lecture and small group teaching (SGT) in a medical curriculum. Four problem-oriented SGT sessions representing diverse topics in the first-year curriculum and their corresponding lectures were videotaped and analyzed using techniques of concept mapping, where the broad concepts from the lectures were identified and matched to the case-specific concepts in the small group sessions. The results show that lectures function as an anchor for the students' discussion of issues relevant to clinical problem-solving and interventions in small group sessions. These discussions extended to contextual aspects of clinical practice that were not dealt with in the lectures, such as ethical/cultural issues around the treatment of patients. Furthermore, small group environments were found to promote discussions that allowed the integration of information from different sources and encompassed concepts across a number of disciplines. These results suggest that carefully designed small group sessions serve the purposes of 1) illustrating broader concepts in lectures to case-specific, clinically relevant problem-solving and 2) promoting knowledge integration from diverse sources of information. The implications of these results for learning and reasoning in health science curricula are discussed.
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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 | 0.011 | 0.219 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".