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Record W1985746577 · doi:10.1300/j013v40n02_04

Acute Forensic Medical Procedures Used Following a Sexual Assault Among Treatment-Seeking Women

2004· article· en· W1985746577 on OpenAlexaff
Hester Dunlap, Paulette Brazeau, Lana Stermac, Mary Addison

Bibliographic record

VenueWomen & Health · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsSexual assaultMedicinePsychiatryForensic scienceInjury preventionSuicide preventionOccupational safety and healthCoercion (linguistics)Poison controlClinical psychologyMedical emergencyPsychology

Abstract

fetched live from OpenAlex

Despite the negative physical and mental health outcomes of sexual assault, a minority of sexually assaulted women seek immediate post-assault medical and legal services. This study identified the number and types of acute forensic medical procedures used by women presenting at a hospital-based urgent care centre between 1997 and 2001 within 72 hours following a reported sexual assault. The study also examined assault and non-assault factors associated with the use of procedures. It was hypothesized that assault characteristics resembling the stereotype of rape would be associated with the use of more procedures. The multiple regression indicated that injury severity, coercion severity, homelessness, and delay in presentation were significantly associated with the number of procedures received. Findings provide partial support for the hypothesis that post-assault procedures would be associated with the stereotype of rape, and highlight homeless women as a group particularly at risk for not receiving adequate medical treatment following a sexual assault.

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)0.004
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
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 integrity0.0000.000
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 head0.030
GPT teacher head0.361
Teacher spread0.331 · 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

Citations12
Published2004
Admission routes1
Has abstractyes

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