Educational interventions to improve emotional intelligence in nursing and medical students: A systematic review
Bibliographic record
Abstract
Purpose: Emotional intelligence (EI) is a trainable skillset and has been shown to have a positive impact on clinician wellbeing, patient outcomes, and other personal and professional factors. This review aimed to evaluate and summarize current intervention strategies designed to improve EI in nursing and medical students.Results: Interventions varied by theme, content, learning activities, and duration. Nine different EI measurement instruments were utilized; learning outcomes were assessed by modified Kirkpatrick classifications. Nine out of 12 studies showed significant positive improvements in EI outcome measures post-intervention. Our review demonstrated moderate to high quality OCEBM level 1b and 2b evidence, moderate quality MERSQI/NOS-E risk of bias appraisal, and GRADE-defined desirable intervention effects with respect to positive modifications in Kirkpatrick identified learner perceptions and attitudes.Conclusions: Nearly all interventions resulted in positive change in EI. The greatest improvements resulted from intervention content relating to self-awareness, empathy, problem-solving, stress coping, and use/management of emotions, involved group-based learning activities, and were delivered in 10-15 hours spread over 8-12 weeks. No specific recommendations can be made about timing of interventions within nursing or medical professional curricula. Further research and development of objective behavioral EI skill assessments and patient outcomes is warranted.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".