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Multilevel elements associated with HIV serosorting for sexual encounters: a scoping literature review

2021· article· en· W3178458761 on OpenAlexaff
Alma Angélica Villa-Rueda, Dora Julia Onofre-Rodríguez, Siobhan Churchill, Fernanda Ramírez-Barajas, Raquel Alicia Benavides-Torres

Bibliographic record

VenueCiência & Saúde Coletiva · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsWestern University
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)MEDLINEMedicineEnvironmental healthPsychologyFamily medicinePolitical science

Abstract

fetched live from OpenAlex

A scoping literature review to identify the multilevel HIV serosorting related elements was developed. Articles from EBSCO, PubMed, PsyNET and Science Direct with serosort* or serosorting at the tittle or abstract, written in English or Spanish were included. No restriction in type of population or design were applied. 239 records were retrieved after duplicates removed, but 181 references were extracted for full-text review. Individual level: HIV knowledge, serostatus, risk perceptions, abilities to disclose and for condom use negotiation, motivations, use of drugs, stigma, attitudes toward condom use, and perceptions/beliefs about the HIV and related treatments, HIV infection rates/testing and behavioral factors. Interpersonal level: social networks, abilities (sexual behavior negotiation, and communication). Community level: stigma, social norms, access to HIV related services. Structural level: political context, HIV related funding and public policies. HIV Serosorting is not solely an interpersonal behavior it involves multilevel elements that must be acknowledged by professionals and stakeholders.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.354
Teacher spread0.320 · 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 designSystematic review
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

Citations7
Published2021
Admission routes1
Has abstractyes

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