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Record W2299303828 · doi:10.1080/10538712.2016.1120258

Psychosocial Profile of Children Having Participated in an Intervention Program for Their Sexual Behavior Problems: The Predictor Role of Maltreatment

2016· article· en· W2299303828 on OpenAlexaff
Anne‐Marie Tougas, Isabelle Boisvert, Marc Tourigny, Annie Lemieux, Claudia Tremblay, Mélanie Gagnon

Bibliographic record

VenueJournal of Child Sexual Abuse · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsCegep regional de LanaudiereUniversité de Sherbrooke
Fundersnot available
KeywordsPsychosocialIntervention (counseling)Sexual abuseNeglectPsychologyClinical psychologyPoison controlChild abuseSuicide preventionPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

This study sought to verify if a history of maltreatment may predict the psychosocial profile of children who participated in an intervention program aiming at reducing sexual behavior problems. Data were collected at both the beginning and the end of the intervention program using a clinical protocol and standardized tests selected on the basis of the intervention targets. In general, the results indicate that children who had experienced maltreatment display a psychosocial profile that is similar to that of children who had not experienced maltreatment. However, children who had experienced psychological abuse or neglect may display greater externalized or sexualized behaviors, whereas children who have a parent who had been a victim of sexual abuse may display fewer sexualized 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 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.025
GPT teacher head0.326
Teacher spread0.301 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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