Exploring cognitive integration of basic science and its effect on diagnostic reasoning in novices
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 the value of basic science in providing critical connections to clinical signs and symptoms while accounting for the fact that clinicians may not spontaneously articulate their use of basic science knowledge in clinical reasoning. In this study we used a diagnostic justification test to explore the impact of integrated basic science instruction on novices' diagnostic reasoning process. Participants were allocated to an integrated basic science or clinical science training group. The integrated basic science group was taught the clinical features along with the underlying causal mechanisms of four musculoskeletal pathologies while the clinical science group was taught only the clinical features. Participants completed a diagnostic accuracy test immediately after initial learning, and one week later a diagnostic accuracy and justification test. The results showed that novices who learned the integrated causal mechanisms had superior diagnostic accuracy and better understanding of the relative importance of key clinical features. These findings further our understanding of cognitive integration by providing evidence of the specific changes in clinical reasoning when basic and clinical sciences are integrated during learning.
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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 | 0.003 | 0.033 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".