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Record W3044797386 · doi:10.3138/cjhs.2020-0014

Sexual violence among men who have sex with men and two-spirit peoples: A scoping review

2020· review· en· W3044797386 on OpenAlexaffvenue
Jessy Dame, John L. Oliffe, Nolan E. Hill, Leah Carrier, Kelsey Evans-Amalu

Bibliographic record

VenueThe Canadian Journal of Human Sexuality · 2020
Typereview
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsDalhousie UniversityUniversity of British Columbia
Fundersnot available
KeywordsMen who have sex with menShamePsychologyAnxietySexual orientationMedicinePsychiatryHuman immunodeficiency virus (HIV)Social psychologyFamily medicine

Abstract

fetched live from OpenAlex

Sexual violence (SV) is a human rights and public health issue that impacts people from all communities. Men who have sex with men/Two-Spirit (MSM/2S) peoples are up to six times more likely to experience SV compared to heterosexual men. In addition, MSM/2S people are less likely to report SV. Reasons for this lack of reporting include male rape myths, fear of judgment, stigma, shame, homophobia, and/or lack of access to supportive services. The current scoping review offers a synthesis of the recent literature regarding SV among MSM/2S peoples in order to make research, practice, and policy recommendations. Findings indicated consistent SV risk factors, associated negative health outcomes, and barriers to accessing services. Risk factors included being younger, inexperience in MSM/2S communities, and having a history of sexual abuse or violence. Negative health outcomes included increased risk for HIV, depression, anxiety, heart disease, fear, eating disorders, substance use, social isolation, PTSD, and suicide. Help-seeking barriers included limited support services, lack of reporting, and male rape myths. Policy recommendations include targeted efforts to raise awareness of SV among MSM/2S peoples, and affirming the legitimacy of reporting SV through specialized healthcare services and trained professionals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.377
Teacher spread0.321 · 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 teacher head, not a consensus.

Study designOther design
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

Citations21
Published2020
Admission routes2
Has abstractyes

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