Fostering clinical reasoning in physiotherapy: comparing the effects of concept map study and concept map completion after example study in novice and advanced learners
Bibliographic record
Abstract
BACKGROUND: Health profession learners can foster clinical reasoning by studying worked examples presenting fully worked out solutions to a clinical problem. It is possible to improve the learning effect of these worked examples by combining them with other learning activities based on concept maps. This study investigated which combinaison of activities, worked examples study with concept map completion or worked examples study with concept map study, fosters more meaningful learning of intervention knowledge in physiotherapy students. Moreover, this study compared the learning effects of these learning activity combinations between novice and advanced learners. METHODS: Sixty-one second-year physiotherapy students participated in the study which included a pre-test phase, a 130-min guided-learning phase and a four-week self-study phase. During the guided and self-study learning sessions, participants had to study three written worked examples presenting the clinical reasoning for selecting electrotherapeutic currents to treat patients with motor deficits. After each example, participants engaged in either concept map completion or concept map study depending on which learning condition they were randomly allocated to. Students participated in an immediate post-test at the end of the guided-learning phase and a delayed post-test at the end of the self-study phase. Post-tests assessed the understanding of principles governing the domain of knowledge to be learned (conceptual knowledge) and the ability to solve new problems that have similar (i.e., near transfer) or different (i.e., far transfer) solution rationales as problems previously studied in the examples. RESULTS: Learners engaged in concept map completion outperformed those engaged in concept map study on near transfer (p = .010) and far transfer (p < .001) performance. There was a significant interaction effect of learners' prior ability and learning condition on conceptual knowledge but not on near and far transfer performance. CONCLUSIONS: Worked examples study combined with concept map completion led to greater transfer performance than worked examples study combined with concept map study for both novice and advanced learners. Concept map completion might give learners better insight into what they have and have not yet learned, allowing them to focus on those aspects during subsequent example study.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".