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Record W2481451939 · doi:10.1177/1524838016659484

Responding to Delayed Disclosure of Sexual Assault in Health Settings: A Systematic Review

2016· review· en· W2481451939 on OpenAlexaff
Stephanie Lanthier, Janice Du Mont, Robín Masón

Bibliographic record

VenueTrauma Violence & Abuse · 2016
Typereview
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsPublic Health OntarioWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsSexual assaultHealth careMedicineSystematic reviewNursingPsychologyFamily medicineMEDLINESuicide preventionPoison controlMedical emergency

Abstract

fetched live from OpenAlex

Few adolescent and adult women seek out formal support services in the acute period (7 days or less) following a sexual assault. Instead, many women choose to disclose weeks, months, or even years later. This delayed disclosure may be challenging to support workers, including those in health-care settings, who lack the knowledge and skills to respond effectively. We conducted a systematic literature review of health-care providers' responses to delayed disclosure by adolescent and adult female sexual assault survivors. Our primary objective was to determine how health-care providers can respond appropriately when presented with a delayed sexual assault disclosure in their practice. Arising out of this analysis, a secondary objective was to document recommendations from the articles for health-care providers on how to create an environment conducive to disclosing and support disclosure in their practice. These recommendations for providing an appropriate response and supporting disclosure are summarized.

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)0.005
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.057
GPT teacher head0.415
Teacher spread0.358 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations73
Published2016
Admission routes1
Has abstractyes

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