The benefits of flexibility: the pedagogical value of instructions to adopt multifaceted diagnostic reasoning strategies
Bibliographic record
Abstract
OBJECTIVES: Building on the advice of previous research to avoid parsing diagnostic strategies too finely, recent studies have shown that teaching novices to utilise analytic and non-analytic reasoning strategies yields higher diagnostic accuracy than teaching either in isolation. This study assesses the extent to which students spontaneously adopt a combined approach and compares its benefits with those experienced with a contrastive learning strategy known to enhance analogical transfer. METHODS: A sample of 48 naïve students were trained to identify features on electrocardiograms (ECGs) and assign diagnoses. Half the participants learned in a standard manner, encountering diagnoses (and their associated features) in sequence. The remaining participants were explicitly instructed to draw comparisons between the diagnostic category being learned and another confusable diagnostic category (contrastive learning). Half the participants in both groups were further instructed to carefully identify all features while trusting guidance provided by feelings of familiarity (a combined reasoning strategy). The remaining participants were given no instructions on how to approach the diagnostic task. RESULTS: Greater diagnostic accuracy was achieved following both contrastive learning and instructions to use a combined reasoning strategy relative to the control conditions. These variables did not interact with each other, nor did they interact with novelty of the test case. The effects were observed immediately after learning and following a 1-week delay. DISCUSSION: The results emphasise the importance of explicitly empowering students to utilise multiple diagnostic strategies, including non-analytic approaches. In addition, this study reveals the benefit that can be gained from contrastive learning in a medical domain.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".