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Record W4410102416 · doi:10.1186/s12939-025-02492-5

Understanding African American/Black and Latine young and emerging adults living with HIV: a sequential explanatory mixed methods study focused on self-regulatory resources

2025· article· en· W4410102416 on OpenAlexfundno aff
Leo Wilton, Marya Gwadz, Charles M. Cleland, Stephanie Campos, Michelle R. Munson, Caroline Dorsen, S. Serrano, Dawa Sherpa, Shaddy K. Saba, Corey Rosmarin-DeStefano, Prema Filippone

Bibliographic record

VenueInternational Journal for Equity in 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
KeywordsBiostatisticsMedicinePublic healthDemographyMen who have sex with menViral loadHealth psychologyPopulationGerontologyEnvironmental healthClinical psychologyPsychologyFamily medicineHuman immunodeficiency virus (HIV)Sociology

Abstract

fetched live from OpenAlex

BACKGROUND: HIV care continuum engagement is inadequate among African American/Black and Latine (AABL) young/emerging adults living with HIV in the United States. Within this population, some subgroups face barriers to research and are under-studied. Grounded in social action theory, the present study focuses on a diverse community-recruited cohort including those with non-suppressed HIV viral load. Using a sequential explanatory mixed methods design, we describe contextual self-regulatory resources (e.g., substance use, mental health), and their relationships to HIV management. METHODS: Participants (N = 271) engaged in structured baseline assessments and biomarker testing (HIV viral load, drug screening). Being well-engaged in HIV care and HIV viral suppression were the primary outcomes. We purposively sampled a subset for maximum variability for in-depth interviews (N = 41). Quantitative data were analyzed via descriptive statistics and logistic regression, and results were used to develop qualitative research questions. Then, qualitative data were analyzed via directed content analysis. The joint display method was used to integrate results. RESULTS: Participants' mean age was 25 years (SD = 2). The majority (59%) were Latine/Hispanic and 41% were African American/Black. Nearly all were assigned male sex at birth (96%) and identified as gay/bisexual/queer (93%). The average HIV diagnosis was 4 years prior (SD = 3). The majority were well-engaged in HIV care (72%) and evidenced viral suppression (81%). Substance use (tobacco, marijuana, alcohol) was prevalent, mainly at low- and moderate-risk levels. Drug screening indicated marijuana, methamphetamine, and MDMA were the most common recent substances. Symptoms of depression and PTSD were associated with decreased odds of engagement in care. High-risk cannabis use was associated with decreased odds of HIV viral suppression. Qualitative results highlighted the prevalence of substance use in social networks and venues, and the importance of substances as a coping strategy, including for mental health distress. Tobacco and methamphetamine (but not marijuana) were described as problematic, and marijuana was used as harm reduction. Substance use was more common among those with non-suppressed versus suppressed HIV viral load. However, overall, substance use did not commonly interfere substantially with HIV management. CONCLUSIONS: The present study advances knowledge on AABL young/emerging adults living with HIV and highlights ways to improve screening and services.

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)0.016
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.001
Scholarly communication0.0020.003
Open science0.0020.003
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 head0.102
GPT teacher head0.481
Teacher spread0.379 · 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 designQualitative
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

Citations3
Published2025
Admission routes1
Has abstractyes

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