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Record W2016153821 · doi:10.1177/1077559513497250

“Because She’s One Who Listens”

2013· article· en· W2016153821 on OpenAlexaff
Lindsay C. Malloy, Sonja P. Brubacher, Michael E. Lamb

Bibliographic record

VenueChild Maltreatment · 2013
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsInterviewSexual abuseChild sexual abuseSelf-disclosurePsychologyChild abuseHealth professionalsSuicide preventionDevelopmental psychologyMedicineClinical psychologyPoison controlSocial psychologyHealth careMedical emergency

Abstract

fetched live from OpenAlex

The current study examined investigative interviews using the National Institute of Child Health and Human Development (NICHD) Investigative Interview Protocol with 204, five- to thirteen-year-old suspected victims of child sexual abuse. The analyses focused on who children told, who they wanted (or did not want) to tell and why, their expectations about being believed, and other general motivations for disclosure. Children's spontaneous reports as well as their responses to interviewer questions about disclosure were explored. Results demonstrated that the majority of children discussed disclosure recipients in their interviews, with 78 children (38%) explaining their disclosures. Only 15 children (7%) mentioned expectations about whether recipients would believe their disclosures. There were no differences between the types of information elicited by interviewers and those provided spontaneously, suggesting that, when interviewed in an open-ended, facilitative manner, children themselves produce informative details about their disclosure histories. Results have practical implications for professionals who interview children about sexual abuse.

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)0.007
metaresearch head score (Gemma)0.021
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 score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.003

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 head0.268
Teacher spread0.242 · 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

Citations96
Published2013
Admission routes1
Has abstractyes

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