Learning-by-Concordance Approach in Health Professions Education: A Scoping Review
Bibliographic record
Abstract
Introduction: Learning by concordance (LbC) is an educational approach designed to develop expertise, particularly in the field of clinical reasoning (CR) among healthcare professionals. It is based on the script concordance test (SCT) with the addition of feedback based on expert responses. The objective of this study was to map the scientific literature on this rapidly growing learning approach. Methods: A scoping review was conducted following the Arksey and O'Malley framework and the PRISMA-ScR guidelines. A systematic search was conducted in MEDLINE, Embase, CINAHL, Web of Science and Google Scholar up to March 10, 2025. Eligible primary studies had to focus on the LbC approach targeting healthcare learners. Results: Twenty-eight studies met the inclusion criteria: twenty focused on the implementation of the LbC approach and eight on its development. Most of the studies used mixed methods (quantitative and qualitative). The results mainly indicate that learners perceive the LbC approach as engaging and beneficial for decision-making. The articles mention five elements related to the development of LbC that would contribute to its success. Discussion: The LbC approach could be applicable to a wide range of disciplines and learning levels. The variability in the procedures for developing the approach, as well as the variability of the objectives and methodologies of the studies, limit the comparability of the results. Conclusion: LbC is a promising approach for promoting decision-making skills in a variety of uncertain clinical contexts. The concept of standardized development and evaluation frameworks for this approach could improve its applicability, effectiveness and reproducibility.
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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 | 0.069 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.038 | 0.036 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".