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Record W4407576842 · doi:10.1186/s12889-025-21869-3

A mixed methods descriptive study of a diverse cohort of African American/Black and Latine young and emerging adults living with HIV: Sociodemographic, background, and contextual factors

2025· article· en· W4407576842 on OpenAlexfundno aff
Marya Gwadz, Leo Wilton, Charles M. Cleland, S. Serrano, Dawa Sherpa, Maria Fernanda Zaldivar, Robert Freeman, Stephanie Campos, Nisha Beharie, Corey Rosmarin-DeStefano, Prema Filippone, Michelle R. Munson

Bibliographic record

VenueBMC Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute on Drug AbuseYork UniversityCenter for Drug Use and HIV Research
KeywordsBiostatisticsMedicineDemographyPublic healthSocioeconomic statusViral loadCohortPopulationGerontologyCohort studyPsychological interventionYoung adultDescriptive statisticsEnvironmental healthFamily medicineHuman immunodeficiency virus (HIV)PsychiatryInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: American/Black and Latine (AABL) young/emerging adults living with HIV in the United States (US) have consistently failed to meet targets for HIV care/medication engagement. Among this population, those with non-suppressed HIV viral load are understudied, along with immigrants and those with serious socioeconomic deprivation. Guided by social action theory, we took a mixed methods approach (sequential explanatory design) to describe sociodemographic, background, and contextual factors, and their relationships to HIV management, among a diverse cohort. METHODS: Participants (N = 271) received structured baseline assessments and HIV viral load testing. Primary outcomes were being well-engaged in HIV care and HIV viral suppression. A subset (N = 41) was purposively sampled for maximum variability for in-depth interviews. Quantitative data were analyzed with descriptive statistics and logistic regression, and used to develop a research question about life contexts. Qualitative data were analyzed with directed content analysis, and the joint display method was used to integrate results. RESULTS: Participants were 25 years old, on average (SD = 2). The majority (59%) were Latine/Hispanic and the reminder African American/Black. Almost all were assigned male sex at birth (96%) and sexual minorities (93%). Half (49%) were born outside the US and 33% spoke primarily Spanish. They were diagnosed with HIV four years prior on average (SD = 3). Most were well-engaged in HIV care (72%) and evidenced viral suppression (81%). Speaking Spanish was associated with a higher odds of care engagement, and adverse childhood experiences and income from federal benefits were associated with a lower odds. None of the factors predicted viral suppression. Qualitative results highlighted both developmentally typical (insufficient financial resources, unstable housing) and atypical challenges (struggles with large bureaucracies, HIV disclosure, daily medication use). Federal benefits and the local HIV social services administration were critical to survival. Immigrant participants came to the US to escape persecution and receive HIV care, but HIV management was often disrupted. Overall qualitative results highlighted both risk and protective factors, and resilience. Qualitative results added detail, nuance, and richness to the quantitative findings. CONCLUSIONS: The present study advances what is known about the backgrounds and contexts of diverse and understudied AABL young/emerging adults living with HIV.

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)0.010
metaresearch head score (Gemma)0.008
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 score0.017
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.071
GPT teacher head0.391
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 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

Citations4
Published2025
Admission routes1
Has abstractyes

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