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Record W4367048533 · doi:10.1093/heapro/daad029

First-time testers in the GetaKit study: conceptualizing new paths to care for gbMSM

2023· article· en· W4367048533 on OpenAlexafffundabout
Patrick O’Byrne, Lance T. McCready, Jason Tigert, Alexandra Musten, Lauren Orser

Bibliographic record

VenueHealth Promotion International · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of TorontoOntario HIV Treatment NetworkUniversity of Ottawa
FundersOntario HIV Treatment Network
KeywordsMen who have sex with menHuman immunodeficiency virus (HIV)Test (biology)PopulationMedicineDemographyIndigenousGerontologyHiv testFamily medicineEnvironmental healthPsychologyHealth servicesSociology

Abstract

fetched live from OpenAlex

When analyzing the data for Ontario, Canada, HIV rates continue to be highest among gay, bisexual and other men who have sex with men (gbMSM). Since HIV diagnosis is a key component of HIV care, self-testing has provided options for allowing this population to access care, resulting in a significant number of first-time testers. Between 1 April 2021 and 31 January 2022, 882 gbMSM participants ordered an HIV self-test through GetaKit. Of these, 270 participants reported that they had never undergone HIV testing previously. Our data showed that first-time testers were generally younger, members of BIPOC (Black, Indigenous and people of color) communities and they reported more invalid test results than those who had tested previously. This suggests that HIV self-testing may be a more successful and appealing component of the HIV prevention armamentarium for this population, but one that is not without its shortcomings as an entry to care.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.112
GPT teacher head0.457
Teacher spread0.345 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations8
Published2023
Admission routes3
Has abstractyes

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