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Record W4280503211 · doi:10.1111/josh.13191

Understanding the Needs of Primary School Teachers in Supporting Their Students' Emotion Regulation

2022· article· en· W4280503211 on OpenAlexaff
Julia Petrovic, Jessica Mettler, Amanda Argento, Dana Carsley, Elana Bloom, Shaun Sullivan, Nancy L. Heath

Bibliographic record

VenueJournal of School Health · 2022
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsConcordia UniversityJohn Abbott CollegeMcGill University
Fundersnot available
KeywordsPerceptionContext (archaeology)School teachersPsychologyMeditationMedical educationMedicinePedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Accumulating evidence has underscored the importance of fostering children's emotion regulation (ER) within primary school settings and the role of teachers in such efforts. This study sought to assess the needs of teachers in supporting students' ER, through a better understanding of teachers' perceptions and use of healthy versus unhealthy ER strategies in the classroom. METHODS: Primary school teachers (n = 212; 91% female) completed an online, researcher-developed needs assessment survey assessing their perceptions regarding the importance of ER instruction and challenges surrounding children's ER, as well as the perceived effectiveness and reported use of healthy and unhealthy ER strategies in the classroom. RESULTS: Cochran's Q and chi-square analyses revealed misperceptions regarding the effectiveness of healthy and unhealthy strategies, as well as discrepancies between teachers' perceptions regarding the effectiveness of specific healthy strategies (eg, meditation) and their reported use of them. CONCLUSIONS: While teachers recognize the growing importance of fostering ER in the classroom, the present findings suggest that there is a need for more professional development regarding the effectiveness and implementation of ER strategies in the primary school context. Efforts should be made to provide teachers with concrete recommendations for the implementation of ER strategies in the classroom.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.092
GPT teacher head0.353
Teacher spread0.261 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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