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Record W1980392619 · doi:10.1007/s40037-012-0017-0

Effects of two different instructional formats on scores and reliability of a script concordance test

2012· article· en· W1980392619 on OpenAlexfundno aff
W. E. Sjoukje Van den Broek, Marianne V. Van Asperen, Olle ten Cate, Gerlof D. Valk

Bibliographic record

VenuePerspectives on Medical Education · 2012
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsnot available
FundersUniversity of AdelaideUniversitair Medisch Centrum UtrechtUniversiteit UtrechtMcGill University
KeywordsConcordanceTest (biology)Reliability (semiconductor)Computer scienceMedical educationPsychologyMedical physicsNatural language processingMedicineInternal medicine

Abstract

fetched live from OpenAlex

The script concordance test (SCT) is designed to assess clinical reasoning by adapting the likelihood of a case diagnosis, based on provided new information. In the standard instructions students are asked to exclude alternative diagnoses they have in mind when answering the questions, but it might be more authentic to include these. Fifty-nine final-year medical students completed an SCT. Twenty-nine were asked to take their differential diagnosis into account (adapted instructions). Thirty students were asked not to consider other diagnoses (standard instructions). All participants were asked to indicate for each question whether they were confused answering it with the given instructions ('confusion indication'). Mean score of the test with the adapted instructions was 81.5 (SD 3.8) and of the test with the standard instructions 82.9 (SD 5.0) (p = 0.220). Cronbach's alpha was 0.39 for the adapted instructions and 0.66 for the standard instructions. The mean number of confusion indications was 4.2 (SD 4.4) per student for the adapted instructions and 16.7 (SD 28.5) for the standard instructions (p = 0.139). Our attempt to improve SCTs reliability by modifying the instructions did not lead to a higher alpha; therefore we do not recommend this change in the instructional format.

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)0.000
metaresearch head score (Gemma)0.123
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 score0.122
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.123
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)0.0000.000

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 head0.010
GPT teacher head0.349
Teacher spread0.338 · 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

Citations12
Published2012
Admission routes1
Has abstractyes

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