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Barriers to PrEP Uptake in Two Spirit, Gay, Bisexual, Trans and Queer Men, and Non-Binary People in Canada

2022· preprint· en· W4294739139 on OpenAlexaffabout
Anthony Theodore Amato, Card Kiffer, Klassen Ben, Vattiata Mik, Michael Kwag, Darrell H. S. Tan, Nathan J. Lachowsky

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsCommunity Based Research CentreSt. Michael's HospitalSimon Fraser UniversityUniversity of Victoria
Fundersnot available
KeywordsDemographyTransgenderMen who have sex with menSexual orientationSexual minorityIndigenousPopulationSexual identityGender studiesLesbianHealth equityPsychologyMedicineGerontologyHuman immunodeficiency virus (HIV)Social psychologyHuman sexualitySociologyFamily medicinePublic health

Abstract

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Background: Indigenous and ethnoracial minority Gay, Bisexual, Trans, Queer men, Two-Spirit, and non-binary (GBTQ2S+) people in Canada are often underrepresented in PrEP uptake within GBTQ2S+ population samples due to health and social inequities. We sought to determine barriers to PrEP use for sub-populations of HIV-negative GBT2Q based on ethnoracial identity and gender diversity. Method: Participants self-completed the national, online, anonymous, community-based Sex Now 2019 behavioural surveillance survey. Recruitment occurred via GBTQ2S+-oriented sex-seeking apps, websites, and social media from November 2019 to February 2020 (pre-COVID). Participants completed questions on demographics and PrEP-related barriers (e.g., low self-perceived HIV risk, cost, judgement from healthcare providers). Multivariable confounder bootstrapped (1000 iterations) logistic regression models assessed differences in various barriers to PrEP by ethnoracial identity, and stratified by cisgender/gender-diverse identity; possible confounders included age, income, and sexual orientation, if significantly correlated with the outcome. Beta coefficients (β) with 95% confidence intervals (CI) are presented. Results: Of 1137 HIV-negative Indigenous and ethnoracial minority GBTQ2S+ participants (85.5% cisgender men, 14.5% gender-diverse), 17.2% were Black/African/Caribbean, 29.2% were Indigenous, 20.0% were Latinx, 28.9% were East/Southeast Asian, and 21.9% were Arab/South Asian. Four ethnoracial differences in PrEP-related barriers were identified. First, low self-perceived HIV risk was less likely to be reported by Latinx (15.6% versus 23.2%, β=-0.75, CI [-1.41,-0.15]) and Arab/South Asian (17.8% versus 22.8%, β=-0.53, CI [-1.10,-0.056]) participants. Second, disliking taking pills was less likely to be reported by Arab/South Asian participants (8.7% versus 16.4%, β=-0.61, CI [-1.29,-0.11]). Third, cost as a barrier was less likely to be reported by Indigenous participants (19.9% versus 28.9%, β=-0.61, CI [-1.16,-0.11]). Fourth, judgement from healthcare providers was less likely reported by gender-diverse South Asian participants (8.0%, β=-1.54, CI [-22.33,-0.024]) versus all other gender-diverse participants (23.6%). Conclusion: Commonly reported PrEP barriers for Indigenous and ethnoracial minority GBTQ2S+ were self-perceived risk, cost, and judgement from healthcare providers. However, specific ethnoracial groups, intersecting with gender diversity, experienced these less. Although this data cannot encapsulate all PrEP barriers faced by these communities, it highlights the need for culturally-appropriate and gender-affirming health promotion strategies, new PrEP prevention efforts, and healthcare provider capacity-building to improve equitable PrEP implementation.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.019
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
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 head0.033
GPT teacher head0.360
Teacher spread0.327 · 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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Citations1
Published2022
Admission routes2
Has abstractyes

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