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Record W2292986784 · doi:10.1080/10538712.2016.1124308

The Disclosure Experiences of Male Child Sexual Abuse Survivors

2016· article· en· W2292986784 on OpenAlexaff
Charlotte Gagnier, Delphine Collin‐Vézina

Bibliographic record

VenueJournal of Child Sexual Abuse · 2016
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsSexual abusePsychologyChild sexual abuseQualitative researchInterpretative phenomenological analysisSelf-disclosureDiversity (politics)Clinical psychologyDevelopmental psychologyChild abuseSuicide preventionPoison controlSocial psychologyMedicine

Abstract

fetched live from OpenAlex

This article explores the diversity in the disclosure process of male survivors of child sexual abuse. Disclosure is a complex process for victims of both genders, however masculine norms and stereotypes have contributed to an environment that often negates the experiences of men. The disclosure process of 17 adult male survivors of child sexual abuse was explored using transcripts of telephone interviews. A combination of two qualitative methodologies, the phenomenological method and interpretive description approach, was used to analyze this secondary data. The results indicated that the majority of the men in the study waited until adulthood to disclose their abuse, with negative stereotypes contributing to their delayed disclosures. In terms of specific experiences with disclosure, the participants found they received both positive and negative responses. These results were consistent with the literature.

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)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0000.004
Research integrity0.0010.002
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 head0.016
GPT teacher head0.276
Teacher spread0.260 · 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 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

Citations139
Published2016
Admission routes1
Has abstractyes

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