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Record W4399627483 · doi:10.1016/j.sel.2024.100048

Keeping the lamp lit: A program profile of a community-based social-emotional training for caregivers and educators

2024· article· en· W4399627483 on OpenAlexafffund
Ruth Speidel, Chanel Tsang, Sian Day, Mirella DiSanto, Alyssa Keel, Diane Phu, Suzana Miletic, Tenneil Dhaliwal, Ashma Saldanha, Xiaotian Michelle Zhang, Tina Malti

Bibliographic record

VenueSocial and Emotional Learning Research Practice and Policy · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTraining (meteorology)Social emotional learningPsychologyMedical educationApplied psychologyMedicineDevelopmental psychologyGeography

Abstract

fetched live from OpenAlex

Developing social-emotional training opportunities for caregivers and professionals can promote higher quality care for children and families in the home and in early years services. Community-based efforts that integrate developmental-relational research into practice using a participatory approach are a growing area of interest and focus. The current paper provides a program profile of RAISE (Research and Practice Partnership: Building Awareness and Increasing Social-Emotional Capacity in the Early Years), a social-emotional training model that uses a bottom-up community-based approach to design and implement a developmental-relational training to strengthen caregivers’ and educators’ capacities to support children’s social-emotional development and mental health. We describe our training development approach, which integrates community engagement efforts with developmental-relational and clinical research, including examples of how participatory approaches may inform curricula development. Finally, we highlight several lessons learned from this training model, with the aim of informing future social-emotional development and practice initiatives.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.171
GPT teacher head0.483
Teacher spread0.313 · 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 designNot applicable
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

Citations2
Published2024
Admission routes2
Has abstractyes

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