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Record W4367603080 · doi:10.18332/popmed/165657

Factors associated with intention to use self-sample collection for HIV and other sexually transmitted and blood-borne infections among men who have sex with men in British Columbia, Canada

2023· article· en· W4367603080 on OpenAlexaboutno aff
Ihoghosa Iyamu, Mark Gilbert, Aidan Ablona, Ben Klassen, Heather Pedersen, Devon Haag, Hsiu‐Ju Chang, Nathan J. Lachowsky

Bibliographic record

VenuePopulation Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)Men who have sex with menSample (material)Data collectionDemographyMedicinePsychologyGerontologyEnvironmental healthFamily medicineStatisticsSyphilis

Abstract

fetched live from OpenAlex

Population Medicine considers the following types of articles:• Research Papers -reports of data from original research or secondary dataset analyses.• Review Papers -comprehensive, authoritative, reviews within the journal's scope.These include both systematic reviews and narrative reviews.• Short Reports -brief reports of data from original research.• Policy Case Studies -brief articles on policy development at a regional or national level.• Study Protocols -articles describing a research protocol of a study.• Methodology Papers -papers that present different methodological approaches that can be used to investigate problems in a relevant scientific field and to encourage innovation.• Methodology Papers -papers that present different methodological approaches that can be used to investigate problems in a relevant scientific field and to encourage innovation.

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)0.003
metaresearch head score (Gemma)0.017
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 score0.018
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.285
Teacher spread0.260 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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