Measuring and Improving Emotional Intelligence in Surgery
Bibliographic record
Abstract
OBJECTIVE: Evaluate how emotional intelligence (EI) has been measured among surgeons and to investigate interventions implemented for improving EI. SUMMARY BACKGROUND: EI has relevant applications in surgery given its alignment with nontechnical skills. In recent years, EI has been measured in a surgical context to evaluate its relationship with measures such as surgeon burnout and the surgeon-patient relationship. METHODS: A systematic review was conducted by searching MEDLINE, EMBASE, CINAHL, and PSYCINFO databases using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. MeSH terms and keywords included "emotional intelligence," "surgery," and "surgeon." Eligible studies included an EI assessment of surgeons, surgical residents, and/or medical students within a surgical context. RESULTS: The initial search yielded 4627 articles. After duplicate removal, 4435 articles were screened by title and abstract and 49 articles proceeded to a full-text read. Three additional articles were found via hand search. A total of 37 articles were included. Studies varied in surgical specialties, settings, and outcome measurements. Most occurred in general surgery, residency programs, and utilized self-report surveys to estimate EI. Notably, EI improved in all studies utilizing an intervention. CONCLUSIONS: The literature entailing the intersection between EI and surgery is diverse but still limited. Generally, EI has been demonstrated to be beneficial in terms of overall well-being and job satisfaction while also protecting against burnout. EI skills may provide a promising modifiable target to achieve desirable outcomes for both the surgeon and the patient. Future studies may emphasize the relevance of EI in the context of surgical teamwork.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".