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Record W2610997453 · doi:10.36834/cmej.36683

The emotional intelligence of pediatric residents – a descriptive cross-sectional study

2017· article· en· W2610997453 on OpenAlexafffundvenueabout
Scott McLeod, Lyn K. Sonnenberg

Bibliographic record

VenueCanadian Medical Education Journal · 2017
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsUniversity of AlbertaUniversity of Calgary
FundersChildren's Hospital FoundationStollery Children’s Hospital FoundationWomen and Children's Health Research InstituteChildren's Health Research Institute
KeywordsEmotional intelligenceComputer sciencePsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Background: Emotional Intelligence (EI) is a type of social intelligence. Excellent scores are achieved by displaying high levels of empathy in interpersonal relationships, strong skills in managing stressful situations as well as other personal competencies. Many of the social competencies that EI describes may have a direct impact on patient care. 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.Methods: This was a cross-sectional study administering the EQ-i 2.0© psychometric instrument to pediatric residents at the University of Alberta.Results: Thirty-five residents completed the EQ-i 2.0© (100% response rate). Their overall EI score was not significantly different than a normative group of college-educated professionals. Residents had relative strengths in the subcategories of Emotional expression, Interpersonal Relationships, Empathy, and Impulse Control (all p<0.05). Areas of relative weakness were in the subcategories of Stress Tolerance, Assertiveness, Independence, and Problem Solving (all 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 to enhance the physician-patient relationship.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0290.000

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.

Opus teacher head0.075
GPT teacher head0.425
Teacher spread0.350 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2017
Admission routes4
Has abstractyes

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