The Emotional Intelligence of Paediatric Residents – A Descriptive Cross-Sectional Study
Bibliographic record
Abstract
Abstract BACKGROUND: Emotional Intelligence (EI) is a type of social intelligence with high scores achieved through factors including being empa-thetic in interpersonal relationships, displaying flexibility in adapting to change, and managing stressful situations. There has been an explosion of research into EI in medicine since many of the social competencies described may have a direct impact on patient care. OBJECTIVES: The objective of this study was to describe EI of pediatric residents and to identify if there are EI skills that should be selected for targeted intervention in this population. DESIGN/METHODS: This was a cross-sectional study administering the EQi-2.0© psychometric instrument to all pediatric residents at a Canadian residency program. Scores were analyzed by year of training, gender, and age. RESULTS: Thirty-five residents completed the EQi-2.0© (100% response rate). Their overall EI score was not significantly different than a normative group of college educated professionals. There was no correlation between overall EI and year of training, gender, or age. Residents had relative strengths in the subcategories Emotional expression, Interpersonal Relationships, Empathy, and Impulse Control (all p<0.05). Areas of relative weakness were in the subcategories Stress Tolerance, Assertiveness, Independence and Problem Solving (all p<0.05). There were significantly higher scores in the subcategories Independence, and Problem Solving for residents over thirty years old when compared to younger residents (both p<0.05). CONCLUSION: The EI of pediatric residents is consistent with that of other professionals. Educational interventions may be useful in the areas of weakness, however, individual score reports vary so emphasizing the individual learning needs of each resident based on their weaknesses may be the best educational intervention to enhance the physician-patient relationship.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".