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Record W1976915199 · doi:10.1016/j.ijgo.2010.02.001

Report of the FIGO Working Group on Sexual Violence/HIV: Guidelines for the management of female survivors of sexual assault

2010· article· en· W1976915199 on OpenAlexaboutno aff
Ruxana Jina, Rachel Jewkes, Stephen Munjanja, José David Ortiz Mariscal, Elizabeth Dartnall

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGuidelineSexual violenceFamily medicinePoison controlReproductive healthOccupational safety and healthSex offenseSuicide preventionMEDLINEHealth careInjury preventionPsychiatryNursingSexual abuseMedical emergencyPopulationPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To review the evidence and provide guidelines on the management of sexual violence against women, specifically, rape. OUTCOMES: Outcomes evaluated include effectiveness of post-rape care provision. EVIDENCE: The MEDLINE database was searched for articles published up to December 2008 on the topic of post-rape care and expert opinion was sought from the Sexual Violence Research Initiative membership. In addition, a search was performed for English-language protocols on Google. One Spanish language protocol was considered in the development of the guidelines. VALUES: The evidence was evaluated by authors and reviewers of the South African Department of Health's sexual assault curriculum, and by members of the FIGO Working Group and recommendations were made according to the guidelines developed by the Canadian Task Force on Preventive Health Care. BENEFITS, HARMS, AND COSTS: Implementation of the recommendations in this Guideline should result in more appropriate management of survivors of sexual violence and better physical and psychological outcomes.

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)0.044
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.079
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0140.009
Science and technology studies0.0020.003
Scholarly communication0.0030.003
Open science0.0110.004
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0060.003

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 head0.080
GPT teacher head0.385
Teacher spread0.305 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations45
Published2010
Admission routes1
Has abstractyes

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