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Record W4200393022 · doi:10.3389/fmed.2021.765489

Creating Clinical Reasoning Assessment Tools in Different Languages: Adaptation of the Pediatric Emergency Medicine Script Concordance Test to Japanese

2021· article· en· W4200393022 on OpenAlexaff
Osamu Nomura, Taichi Itoh, Takaaki Mori, Takateru Ihara, Satoshi Tsuji, Nobuaki Inoue, Benoit Carrière

Bibliographic record

VenueFrontiers in Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
FundersLastentautien TutkimussäätiöJapan Foundation for Pediatric Research
KeywordsConcordanceGeneralizability theoryTest (biology)Inter-rater reliabilityContext (archaeology)Pediatric emergency medicineCronbach's alphaDocumentationMedical educationMedical diagnosisMedicinePsychologyReliability (semiconductor)Emergency departmentComputer scienceClinical psychologyPsychometricsNursingEmergency physician

Abstract

fetched live from OpenAlex

Introduction: Clinical reasoning is a crucial skill in the practice of pediatric emergency medicine and a vital element of the various competencies achieved during the clinical training of resident doctors. Pediatric emergency physicians are often required to stabilize patients and make correct diagnoses with limited clinical information, time and resources. The Pediatric Emergency Medicine Script Concordance Test (PEM-SCT) has been developed specifically for assessing physician's reasoning skills in the context of the uncertainties in pediatric emergency practice. In this study, we developed the Japanese version of the PEM-SCT (Jpem-SCT) and confirmed its validity by collecting relevant evidence. Methods: The Jpem-SCT was developed by translating the PEM-SCT into Japanese using the Translation, Review, Adjudication, Pretest, Documentation team translation model, which follows cross-cultural survey guidelines for proper translation and cross-cultural and linguistic equivalences between the English and Japanese version of the survey. First, 15 experienced pediatricians participated in the pre-test session, serving as a reference panel for modifying the test descriptions, incorporating Japanese context, and establishing the basis for the scoring process. Then, a 1-h test containing 60 questions was administered to 75 trainees from three academic institutions. Following data collection, we calculated the item-total correlations of the scores to optimize selection of the best items in the final version of the Jpem-SCT. The reliability of the finalized Jpem-SCT was calculated using Cronbach's α coefficient for ensuring generalizability of the evidence. We also conducted multiple regression analysis of the test score to collect evidence on validity of the extrapolation. Results: The final version of the test, based on item-total correlation data analysis, contained 45 questions. The participant's specialties were as follows: Transitional interns 12.0%, pediatric residents 56.0%, emergency medicine residents 25.3%, and PEM fellows 6.7%. The mean score of the final version of the Jpem-SCT was 68.6 (SD 9.8). The reliability of the optimized test (Cronbach's α) was 0.70. Multiple regression analysis showed that being a transitional intern was a negative predictor of test scores, indicating that clinical experience relates to performance on the Jpem-SCT. Conclusion: This pediatric emergency medicine Script Concordance Test was reliable and valid for assessing the development of clinical reasoning by trainee doctors during residency training.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.037
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.051
GPT teacher head0.401
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2021
Admission routes1
Has abstractyes

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