Experts’ responses in script concordance tests: a response process validity investigation
Bibliographic record
Abstract
CONTEXT: The script concordance test (SCT), designed to measure clinical reasoning in complex cases, has recently been the subject of several critical research studies. Amongst other issues, response process validity evidence remains lacking. We explored the response processes of experts on an SCT scoring panel to better understand their seemingly divergent beliefs about how new clinical data alter the suitability of proposed actions within simulated patient cases. METHODS: A total of 10 Argentine gastroenterologists who served as the expert panel on an existing SCT re-answered 15 cases 9 months after their original panel participation. They then answered questions probing their reasoning and reactions to other experts' perspectives. RESULTS: The experts sometimes noted they would not ordinarily consider the actions proposed for the cases at all (30/150 instances [20%]) or would collect additional data first (54/150 instances [36%]). Even when groups of experts agreed about how new clinical data in a case affected the suitability of a proposed action, there was often disagreement (118/133 instances [89%]) about the suitability of the proposed action before the new clinical data had been introduced. Experts reported confidence in their responses, but showed limited consistency with the responses they had given 9 months earlier (linear weighted kappa = 0.33). Qualitative analyses showed nuanced and complex reasons behind experts' responses, revealing, for example, that experts often considered the unique affordances and constraints of their varying local practice environments when responding. Experts generally found other experts' alternative responses moderately compelling (mean ± standard deviation 2.93 ± 0.80 on a 5-point scale, where 3 = moderately compelling). Experts switched their own preferred responses after seeing others' reasoning in 30 of 150 (20%) instances. CONCLUSIONS: Expert response processes were not consistent with the classical interpretation and use of SCT scores. However, several fruitful and justifiable alternatives for the use of SCT-like methods are proposed, such as to guide assessments for learning.
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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 | 0.002 | 0.336 |
| 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".