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Record W4385407095 · doi:10.1007/s10461-023-04121-0

Systematic Review and Meta-analysis of Linkage to HIV Care Interventions in the United States, Canada, and Ukraine (2010–2021)

2023· review· en· W4385407095 on OpenAlexaboutno aff
Julie H. Levison, Paola Del Cueto, Jaime Vladimir Mendoza, Dina Ashour, Melis Lydston, Kenneth A. Freedberg, Fatma M. Shebl

Bibliographic record

VenueAIDS and Behavior · 2023
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesHarvard University Center for AIDS Research
KeywordsMedicinePsychological interventionCINAHLLinkage (software)Meta-analysisCochrane LibraryIncidence (geometry)Cumulative incidenceFamily medicineConfidence intervalDemographyInternal medicineNursingGeneticsBiologyCohort

Abstract

fetched live from OpenAlex

We conducted a systematic review and meta-analysis of interventions targeting linkage to HIV care in the US, Canada, and Europe. We searched six databases (PubMed, Embase, Cochrane Library, Web of Science and CINAHL). Inclusion criteria were English language studies in adults in the US, Canada, or Europe, published January 1, 2010 to January 1, 2021. We synthesized interventions by type and linkage to care outcome. The outcome was cumulative incidence of 3-month linkage. We estimated cumulative incidence ratios of linkage with 95% confidence intervals (CIs). We screened 945 studies; 13 met selection criteria (n = 1 from Canada, n = 1 from Ukraine, n = 11 from the US) and were included after full text review (total 37,549 individuals). The cumulative incidence of 3-month linkage in the intervention group was 0.82 (95% CI 0.68-0.94) and control group 0.71 (95% CI 0.50-0.90); cIR of linkage for intervention versus control was 1.30 (95% CI 1.13, 1.49). Interventions to improve linkage to care after HIV diagnosis warrant further attention.

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)0.018
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.048
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.021
Bibliometrics0.0110.012
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
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 head0.146
GPT teacher head0.429
Teacher spread0.283 · 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 designMeta-analysis
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
Published2023
Admission routes1
Has abstractyes

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