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Record W4224863216 · doi:10.1186/s12889-022-13153-5

A national recruitment strategy for HIV-serodiscordant partners living in Canada for the Positive Plus One study: a mixed-methods study

2022· article· en· W4224863216 on OpenAlexafffundabout
Min Xi, Sandra Bullock, Joshua Mendelsohn, James Iveniuk, Veronika Moravan, Ann N. Burchell, Darrell H. S. Tan, Amrita Daftary, Tamara Thompson, Bertrand Lebouché, Laura Bisaillon, Ted Myers, Liviana Calzavara

Bibliographic record

VenueBMC Public Health · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsThe Scarborough HospitalSt. Michael's HospitalMcGill University Health CentreMcGill UniversityDouglas CollegeStatistics CanadaYork UniversityPublic Health OntarioToronto Rehabilitation InstituteCentre for Global Health ResearchUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsSerodiscordantMedicineBiostatisticsPsychological interventionDyadFamily medicineHuman immunodeficiency virus (HIV)Public healthDemographyGerontologyPsychologyNursingSocial psychologyViral loadAntiretroviral therapy

Abstract

fetched live from OpenAlex

BACKGROUND: With the recent shift in focus to addressing HIV risk within relationships and couple-based interventions to prevent HIV transmission, successful recruitment of individuals involved in HIV-serodiscordant relationships is crucial. This paper evaluates methods used by the Positive Plus One (PP1) study to recruit and collect data on a diverse national sample of dyads and individuals involved in current or past HIV-serodiscordant relationships, discusses the strengths and limitations of the recruitment approach, and makes recommendations to inform the interpretation of study results and the design of future studies. METHODS: PP1 used a multi-pronged approach to recruit adults involved in a current or past HIV-serodiscordant relationship in Canada from 2016 to 2018 to complete a survey and an interview. Upon survey completion, index (first recruited) partners were invited to recruit their primary current HIV-serodiscordant partner. We investigated participant enrollment by recruitment source, participant-, relationship-, and dyad-level sociodemographic characteristics, missing data, and correlates of participation for individuals recruited by their partners. RESULTS: We recruited 613 participants (355 HIV-positive; 258 HIV-negative) across 10 Canadian provinces, including 153 complete dyads and 307 individuals who participated alone, and representing 460 HIV-serodiscordant relationships. Among those in current relationships, HIV-positive participants were more likely than HIV-negative participants to learn of the study through an ASO staff member (36% v. 20%, p < 0.001), ASO listserv/newsletter (12% v. 5%, p = 0.007), or physician/staff at a clinic (20% v. 11%, p = 0.006). HIV-negative participants involved in current relationships were more likely than HIV-positive participants to learn of the study through their partner (46% v. 8%, p < 0.001). Seventy-eight percent of index participants invited their primary HIV-serodiscordant partner to participate, and 40% were successful. Successful recruitment of primary partners was associated with longer relationship duration, higher relationship satisfaction, and a virally suppressed HIV-positive partner. CONCLUSIONS: Our findings provide important new information on and support the use of a multi-pronged approach to recruit HIV-positive and HIV-negative individuals involved in HIV-serodiscordant relationships in Canada. More creative strategies are needed to help index partners recruit their partner in relationships with lower satisfaction and shorter duration and further minimize the risk of "happy couple" bias.

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)0.023
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0100.001
Scholarly communication0.0030.001
Open science0.0040.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.257
GPT teacher head0.503
Teacher spread0.246 · 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 designQualitative
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

Citations4
Published2022
Admission routes3
Has abstractyes

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