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Record W2749933329 · doi:10.1093/cs/cdx017

Trauma and Early Adolescent Development: Case Examples from a Trauma-Informed Public Health Middle School Program

2017· article· en· W2749933329 on OpenAlexaff
Jason S. Frydman, Christine Mayor

Bibliographic record

VenueChildren & Schools · 2017
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSocioemotional selectivity theoryPsychoeducationPsychologyPublic healthIntervention (counseling)Developmental psychologyCognitive developmentCognitionClinical psychologyMedical educationMedicinePsychiatryNursing

Abstract

fetched live from OpenAlex

Middle-school-age children are faced with a variety of developmental tasks, including the beginning phases of individuation from the family, building peer groups, social and emotional transitions, and cognitive shifts associated with the maturation process. This article summarizes how traumatic events impair and complicate these developmental tasks, which can lead to disruptive behaviors in the school setting. Following the call by Walkley and Cox for more attention to be given to trauma-informed schools, this article provides detailed information about the Animating Learning by Integrating and Validating Experience program: a school-based, trauma-informed intervention for middle school students. This public health model uses psychoeducation, cognitive differentiation, and brief stress reduction counseling sessions to facilitate socioemotional development and academic progress. Case examples from the authors’ clinical work in the New Haven, Connecticut, urban public school system are provided.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.329
Teacher spread0.237 · 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 designCase report
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

Citations39
Published2017
Admission routes1
Has abstractyes

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