Speed kills? Speed, accuracy, encapsulations and causal understanding
Bibliographic record
Abstract
BACKGROUND: The role of basic science, which provides causal explanations for clinical phenomena in medical education, is poorly understood. Schmidt has postulated that expert clinicians maintain this knowledge in 'encapsulated' form, indexed by words or phrases describing the processes. In the present paper we show that students who learn causal explanations have a more coherent understanding of the relation between diseases and clinical features which, in turn, influences recognition of words or phrases describing 'encapsulated knowledge' and the ability to maintain performance under speeded conditions. HYPOTHESES: In comparison to students who simply learn the features of 4 diagnostic categories, students who learn a causal explanation will: (a) recognise words describing encapsulated knowledge more accurately and (b) maintain or improve diagnostic performance under speeded conditions. METHODS: Two studies were conducted involving 4 'pseudo-endocrinology' diseases and undergraduate psychology students. One group learned signs and symptoms alone; the second group also learned a causal explanation. In study 1, they were then given a recognition memory task. In study 2, they were asked to diagnose new cases either (i) as quickly as possible or (ii) taking their time. RESULTS: In study 1, while there was no difference in recognising old words (90% versus 91%), the causal group was better able to recognise encapsulated and novel consistent words (50% versus 41%) (P = 0.02). In study 2 there was an interaction; causal students performed better under speeded conditions (71% versus 66%) but worse under thoroughness conditions (67% versus 73%), as predicted. CONCLUSIONS: Causal understanding leads to more coherent understanding of clinical conditions, which in turn leads to expert-like behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".