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Record W2600069539 · doi:10.1080/10538712.2017.1280577

Posttraumatic Stress Disorder and Suicidal Ideation Among Sexually Abused Adolescent Girls: The Mediating Role of Shame

2017· article· en· W2600069539 on OpenAlexafffund
Stéphanie Alix, Louise Cossette, Martine Hébert, Mireille Cyr, Jean‐Yves Frappier

Bibliographic record

VenueJournal of Child Sexual Abuse · 2017
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-JustineUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsShameSuicidal ideationPsychologyClinical psychologySexual abusePsychological interventionPoison controlPsychiatrySuicide preventionMedicineMedical emergency

Abstract

fetched live from OpenAlex

Sexual abuse is associated with a host of negative repercussions in adolescence. Yet the possible mechanisms linking sexual abuse and negative outcomes are understudied. The purpose of this study was to investigate the relationships among self-blame, shame, coping strategies, posttraumatic stress disorder, depressive symptoms, and suicidal ideation. The sample included 147 sexually abused adolescent girls between 14 and 18 years of age. A total of 66% of girls reached clinical score for posttraumatic stress disorder, and 53% reached clinical score for depressive symptoms. Close to half (46%) reported suicidal thoughts in the past 3 months. Shame was found to partially mediate the relationship between self-blame and posttraumatic stress disorder. Shame and depressive symptoms were also found to partially mediate the relationship between self-blame and suicidal ideation. Results suggest that shame is a crucial target in interventions designed for sexually abused adolescent girls.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.017
GPT teacher head0.289
Teacher spread0.272 · 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

Citations57
Published2017
Admission routes2
Has abstractyes

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