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Record W4394758853 · doi:10.1002/jad.12317

Breathing life into social emotional learning programs: A Bio‐Psycho‐Social approach to risk reduction and positive youth development

2024· article· en· W4394758853 on OpenAlexaff
Ronnie I. Newman, Odilia Yim, Maria‐Christina Stewart

Bibliographic record

VenueJournal of Adolescence · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Toronto
FundersU.S. Department of Education
KeywordsPsychologyPositive Youth DevelopmentNormativeDevelopmental psychologySocial emotional learningFlourishingPsychosocialClinical psychologySocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

INTRODUCTION: Over one-third of US adolescents engage in health risk and problem behaviors. Additionally, significant percentages of problem-free youth aren't flourishing. Left unaddressed, the lifetime mental/physical health and financial burdens may be substantial. Social-Emotional Learning (SEL) and Positive Youth Development (PYD) programs have proliferated to address the drivers of adaptive versus risk behaviors. Research suggests SEL/PYD program outcomes can be improved by adding techniques that physiologically induce calmness, yet few studies exist. METHODS: This randomized controlled trial of 79 urban eighth-graders examined a standardized bio-psycho-social program, SKY Schools, which incorporates a physiologically calming component: controlled yogic breathing. RESULTS: Repeated-measures ANOVAs demonstrated that compared to controls, SKY graduates exhibited significant improvements in emotion regulation, planning and concentration, and distractibility. After 3 months, significant improvements were evidenced in emotion regulation, planning and concentration, identity formation, and aggressive normative beliefs. CONCLUSION: SEL/PYD programs may benefit by incorporating biologically-calming techniques to enhance well-being and prevent risk/problem behaviors.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.031
GPT teacher head0.299
Teacher spread0.269 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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