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Record W3210182063 · doi:10.1177/08295735211051825

Developing Emotional and Social Competencies in Children: Evaluating the Impact of a Classroom-Based Program

2021· article· en· W3210182063 on OpenAlexaffabout
Emily Storey-Hurtubise, Jen Forristal, Colin T. Henning, James D. A. Parker

Bibliographic record

VenueCanadian Journal of School Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsTrent University
Fundersnot available
KeywordsPsychologyEmotional intelligenceCurriculumSocial skillsSocial emotional learningTest (biology)Medical educationDevelopmental psychologyClinical psychologyApplied psychologyPedagogy

Abstract

fetched live from OpenAlex

The relationship between Emotional Intelligence (EI) and numerous positive outcomes has sparked considerable interest from educators and researchers in training and promoting various emotional and social competencies in youth. The present study evaluated the effectiveness of a new school-based program for elementary school students designed to develop various EI-related competencies in children—the “Umbrella Project.” Five hundred and twenty-nine students (44% boys) who attended six schools in the Waterloo, Ontario area, completed a self-report measure of EI before and after participating in the unique training program. Total EI and a majority of EI subscales showed significant improvement from pretest to post test. Girls also showed higher total EI and interpersonal scores regardless of assessment session. The results are very encouraging and suggest regular classroom-based resources and curriculum activities can foster the development of a cross-section of emotional and social competencies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.135
GPT teacher head0.454
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2021
Admission routes2
Has abstractyes

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