Knowledge Integration and Reasoning as a Function of Instruction in a Hybrid Medical Curriculum
Bibliographic record
Abstract
This study investigates the effect of curricular change on knowledge integration and reasoning processes during problem-solving by medical students. The curricular change involved the introduction of problem-based, small group tutorials into a conventional health science curriculum (CC). Students at three levels of training were asked to provide diagnostic explanations of two clinical cases, both before (spontaneous) and after (primed) being exposed to basic science information relevant to the clinical problems. Data were analyzed using techniques of propositional and semantic analysis. Based on theories of instruction and cognition, we expected that the instructional changes would facilitate knowledge integration and influence the reasoning patterns of the students. The results show that students generated fewer inferences and used more information from the basic science text (text-based) to explain the clinical problems. However, they generated a greater number of elaborations during explanations using a mixture of data-driven and hypothesis-driven strategies. The spontaneous and primed problem-solving conditions produced more hypothesis-driven and data-driven strategies, respectively, as would be expected in a hybrid curriculum. We conclude that a) problem-based, small group tutorials facilitate integration of clinical and biomedical knowledge through the use of elaborations and hypothesis-driven strategies, and b) aspects of problem-based learning can be successfully integrated into traditional curricula.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".