Explicit Connections Between Anatomy and Clinical Science are Key to Successful Cognitive Integration
Bibliographic record
Abstract
Introduction The importance of integrated basic and clinical science knowledge is well recognized across the health professions; however, supporting the development of learner’s integrated knowledge in day‐to‐day teaching continues to be an educational challenge. A number of experimental studies show that learners can benefit when instruction is designed to support to support cognitive integration. This type of integration occurs at the individual session‐level and can be contrasted with integration efforts that occur within the curriculum. To date, there remains limited research on specific strategies that educators can use to support learners in achieving cognitive integration during individual teaching sessions. In a series of studies, we examine the relative impact of integrating anatomy and clinical science instruction with different learning strategies on novices learning diagnosis of musculoskeletal (MSK) pathologies. Methods In each study, novice learners were taught four MSK upper limb pathologies using different instructional approaches (integrated anatomy and clinical science, segregated anatomy and clinical science, or clinical science only) combined with additional learning strategies (focused self‐explanation, feature counting, holistic self‐explanation, or worked‐examples). Integrated instruction involved explicitly teaching the underlying causal mechanisms for the signs and symptoms associated with each MSK pathology, whereas no explicit linkages were provided in the segregated or clinical science only learning conditions. Immediately after learning and one‐week later, learners completed a diagnostic accuracy test. Results The findings of these studies showed that novices who learned the MSK pathologies using integrated instructional materials developed superior diagnostic abilities compared to those in the segregated or clinical science only learning groups (p < 0.05). Further, learners who also engaged in holistic self‐explanation while learning with integrated instructional materials scored higher on the diagnostic accuracy test compared those who simply read through worked‐examples that highlighted the underlying anatomical pathology associated with each MSK condition (p < 0.05). Accuracy on the holistic self‐explanation task was also positively correlated with learners diagnostic scores one‐week after initial learning (r = 0.457). Conclusions Our findings demonstrate the value of providing explicit connections between anatomy and clinical science in supporting deep learning in novices. This research also highlights the value of designing instruction that supports cognitive integration and demonstrates that one to maximize learning of anatomy is to use it as a tool to help learners more effectively understand and organize clinical concepts. Further, our findings suggest that learning strategies that emphasize the explicit connections between anatomy and clinical science in a holistic way, hold the potential to support the development of learners’ conceptual knowledge.
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".