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Record W2194074858 · doi:10.2196/jmir.4503

Online Outreach Services Among Men Who Use the Internet to Seek Sex With Other Men (MISM) in Ontario, Canada: An Online Survey

2015· article· en· W2194074858 on OpenAlexafffundabout
David J. Brennan, Nathan J. Lachowsky, Georgi Georgievski, B. R. Simon Rosser, Duncan MacLachlan, James Murray

Bibliographic record

VenueJournal of Medical Internet Research · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of British ColumbiaMinistry of Health and Long Term CareAIDS VancouverOntario HIV Treatment NetworkUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario HIV Treatment NetworkUniversity of Guelph
KeywordsOutreachThe InternetMen who have sex with menInternet privacyHuman immunodeficiency virus (HIV)Reproductive healthPsychologyMedicineFamily medicineWorld Wide WebPopulationComputer sciencePolitical scienceEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Men who use the Internet to seek sex with other men (MISM) are increasingly using the Internet to find sexual health information and to seek sexual partners, with some research suggesting HIV transmission is associated with sexual partnering online. Aiming to "meet men where they are at," some AIDS service organizations (ASOs) deliver online outreach services via sociosexual Internet sites and mobile apps. OBJECTIVE: To investigate MISM's experiences and self-perceived impacts of online outreach. METHODS: From December 2013 to January 2014, MISM aged 16 years or older were recruited from Internet sites, mobile apps, and ASOs across Ontario to complete a 15-minute anonymous online questionnaire regarding their experience of online outreach. Demographic factors associated with encountering online outreach were assessed using backward-stepwise multivariable logistic regression (P<.05 was considered significant). RESULTS: Of 1830 MISM who completed the survey, 8.25% (151/1830) reported direct experience with online outreach services. Encountering online outreach was more likely for Aboriginal versus white MISM, MISM from Toronto compared with MISM from either Eastern or Southwestern Ontario, and MISM receiving any social assistance. MISM who experienced online outreach felt the service provider was friendly (130/141, 92.2%), easy to understand (122/140, 87.1%), helpful (115/139, 82.7%), prompt (107/143, 74.8%), and knowledgeable (92/134, 68.7%); half reported they received a useful referral (49/98, 50%). Few MISM felt the interaction was annoying (13/141, 9.2%) or confusing (18/142, 12.7%). As a result of their last online outreach encounter, MISM reported the following: better understanding of (88/147, 59.9%) and comfort with (75/147, 51.0%) their level of sexual risk; increased knowledge (71/147, 48.3%); and feeling less anxious (51/147, 34.7%), better connected (46/147, 31.3%), and more empowered (40/147, 27.2%). Behaviorally, they reported using condoms more frequently (48/147, 32.7%) and effectively (35/147, 23.8%); getting tested for HIV (43/125, 34.4%) or STIs (42/147, 28.6%); asking for their partners' HIV statuses (37/147, 25.2%); and serosorting (26/147, 17.7%). Few MISM reported no changes (15/147, 10.2%) and most would use these services again (98/117, 83.8%). Most MISM who did not use online outreach said they did not need these services (1074/1559, 68.89%) or were unaware of them (496/1559, 31.82%). CONCLUSIONS: This is the first online outreach evaluation study of MISM in Canada. Online outreach services are a relatively new and underdeveloped area of intervention, but are a promising health promotion strategy to provide service referrals and engage diverse groups of MISM in sexual health education.

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)0.001
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 score0.022
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.151
GPT teacher head0.419
Teacher spread0.268 · 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".

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Citations21
Published2015
Admission routes3
Has abstractyes

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