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Record W2913187706 · doi:10.1002/pits.22236

Child victims of sexual abuse: Teachers' evaluation of emotion regulation and social adaptation in school

2019· article· en· W2913187706 on OpenAlexafffund
Laetitia Mélissande Amédée, Amélie Tremblay‐Perreault, Martine Hébert, Chantal Cyr

Bibliographic record

VenuePsychology in the Schools · 2019
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsPsychologyMediationSexual abuseChild sexual abuseDevelopmental psychologyContext (archaeology)Child abuseSocial environmentClinical psychologySocial competencePoison controlSuicide preventionSocial changeMedicine

Abstract

fetched live from OpenAlex

Abstract This study assessed the social adaptation of sexually abused children and tested whether children's emotion regulation competencies mediated the association between child sexual abuse (CSA) and two outcomes of the social domain (i.e. withdrawal and social difficulties). A group of 283 child victims of sexual abuse and a comparison group composed of 60 nonabused children was recruited. Teachers completed questionnaires assessing the children's emotion regulation competencies, withdrawal, and social difficulties exhibited in the school context. Results showed that sexually abused children displayed poorer emotion regulation skills and higher levels of both withdrawal and social difficulties relative to nonabused children. CSA was associated with social difficulties and withdrawn behavior through the mediation of emotion regulation competencies. Teachers and school psychologists should be assisted in identifying children at risk of social difficulties and emotional dysregulation and schools be encouraged to adopt a trauma‐informed approach.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.044
GPT teacher head0.355
Teacher spread0.312 · 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

Citations37
Published2019
Admission routes2
Has abstractyes

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