Impact of emotional competence on physicians’ clinical reasoning: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Clinical reasoning (CR) is a key competence for physicians and a major source of damaging medical errors. Many strategies have been explored to improve CR quality, most of them based on knowledge enhancement, cognitive debiasing and the use of analytical reasoning. If increasing knowledge and fostering analytical reasoning have shown some positive results, the impact of debiasing is however mixed. Debiasing and promoting analytical reasoning have also been criticised for their lack of pragmatism. Alternative means of increasing CR quality are therefore still needed. Because emotions are known to influence the quality of reasoning in general, we hypothesised that emotional competence (EC) could improve physicians' CR. EC refers to the ability to identify, understand, express, regulate and use emotions. The influence of EC on CR remains unclear. This article presents a scoping review protocol, the aim of which will be to describe the current state of knowledge concerning the influence of EC on physicians' CR, the type of available literature and finally the different methods used to examine the link between EC and CR. METHOD AND ANALYSIS: , describing five major components of EC (identify, understand, express, regulate and use emotions). The concept of CR will include terms related to its processes and outcomes. Context will include real or simulated clinical situations. The search for primary sources and reviews will be conducted in MEDLINE (via Ovid), Scopus and PsycINFO. The grey literature will be searched in the references of included articles and in OpenGrey. Study selection and data extraction will be conducted using the Covidence software. Search and inclusion results will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping review model (PRISMA-ScR). ETHICS AND DISSEMINATION: There are no ethical or safety concerns regarding this review. REGISTRATION DETAILS: OSF Registration DOI: https://doi.org/10.17605/OSF.IO/GM7YD.
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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 | 0.109 | 0.105 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.010 |
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".