Development of a Method to Measure Clinical Reasoning in Pediatric Residents: The Pediatric Script Concordance Test
Bibliographic record
Abstract
Introduction: The Script Concordance Test (SCT) is an assessment method of clinical reasoning skills. SCT is designed to assess a candidate’s ability to reason when faced with decisions encountered in the three phases of clinical decision-making: diagnosis, investigation and treatment. Challenges have been raised related to psychometric properties of SCT scores. Data about accept- ability of the SCT method are also needed. Objectives: 1) To examine the validity of a Pediatric Script Concordance Test (PSCT) in discriminating clinical reasoning ability between junior postgraduate year (PGY) 1 - 2 and senior PGY 3 - 4 pediatric residents, and pediatricians, 2) To determine if higher reliability could be achieved by applying specific test design strategies to the PSCT and 3) To explore trainees’/physicians’ acceptability of the PSCT. Methods: A 24-case/137 question PSCT was administered to 91 residents from four Canadian training centers. Each resident’s PSCT was scored based on the aggregate responses of 21 pediatricians (Panel of Experts (POE)). ANOVA was used to compare across the 3 levels of experience. Reliability was calculated using Cronbach’s α coefficient. Participants completed a post-test survey about the acceptability of PSCT. Results: Overall, a statistical difference in performance was noted across all levels of experience, F = 22.84 (df = 2); p α) was 0.85. Participants expressed keen interest and engagement in the PSCT. Conclusions: PSCT is a valid, reliable, feasible and acceptable method to assess the core competency of clinical reasoning. We suggest the PSCT may be effectively integrated into formative residency assessment and with increasing exposure, experience and refinement may soon be ready to pilot within summative assessments in pediatric medical education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".