Creating Instruction to Optimize Learning of the Anatomical Sciences
Bibliographic record
Abstract
Integration of basic and clinical science knowledge is increasingly being recognized as important for practice in the health professions. The concept of ‘cognitive integration’ places emphasis on role of basic science in providing critical connections to clinical signs and symptoms while accounting for the fact that clinicians may not explicitly articulate their use of basic science knowledge in clinical reasoning. In this study, we aimed to extend previous work on cognitive integration using new learning materials teaching musculoskeletal pathologies with allied health students. In addition, we aimed to further our understanding of cognitive integration and conceptual coherence by using a diagnostic justification task to investigate the impact of integrated basic science instruction on novices' diagnostic reasoning process. Participants (N = 43) were randomly assigned to a integrated basic science (BaSci) or clinical science (CS) training group. The BaSci group was taught the clinical features along with the underlying causal mechanisms of four musculoskeletal pathologies while the CS group was taught only the clinical features. To equalize the learning time between the two conditions, the CS group was taught epidemiology and potential treatment options for each of the pathologies. Participants completed a diagnostic accuracy and memory test immediately after learning and one‐week later. A diagnostic justification test was also completed one‐week after initial learning. Novices who learned the integrated causal mechanisms had superior diagnostic accuracy (p<0.01) and a better understanding of the relative importance of key clinical features (p<0.01). Although participants from both groups identified correct features on the justification test, those in the BaSci group identified key diagnostic features rather than features that were common across disease categories. Participants in the BaSci group also outperformed the CS group on the basic memory test; however, this difference was no longer evident one‐week later. This study demonstrates the positive impact of integrating basic anatomical education and clinical science instruction on students' diagnostic reasoning ability in addition to diagnostic accuracy. These findings further our understanding of conceptual coherence by providing explicit evidence of the advantage learners have when basic science knowledge is cognitively integrated. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".