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Record W2917561174 · doi:10.1111/medu.13814

Experts’ responses in script concordance tests: a response process validity investigation

2019· article· en· W2917561174 on OpenAlexaff
Matthew Lineberry, Eduardo Hornos, Eduardo Pleguezuelos, José Mella, Carlos Brailovsky, Georges Bordage

Bibliographic record

VenueMedical Education · 2019
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsCollege of Family Physicians of Canada
Fundersnot available
KeywordsConcordanceContext (archaeology)Consistency (knowledge bases)Test (biology)PsychologyProcess (computing)Action (physics)Qualitative propertyApplied psychologyMedicineSocial psychologyComputer scienceArtificial intelligenceMachine learning

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.336
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.336
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.403
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2019
Admission routes1
Has abstractyes

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