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Record W4213037852 · doi:10.1097/jnc.0000000000000299

A Qualitative Study on the Social Determinants of HIV Treatment Engagement Among Black Older Women Living With HIV in the Southeastern United States

2022· article· en· W4213037852 on OpenAlexaff

Bibliographic record

VenueJournal of the Association of Nurses in AIDS Care · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQualitative researchHuman immunodeficiency virus (HIV)Social supportSocial determinants of healthIntervention (counseling)Public healthHealth careQualitative property

Abstract

fetched live from OpenAlex

ABSTRACT: Black older women living with HIV (BOWLH) in the United States are disproportionately affected by HIV infection and poor treatment engagement rates, often caused by multiple social determinants of health. In this descriptive qualitative study, we interviewed 17 BOWLH to investigate the facilitators and barriers to HIV treatment engagement. Data were analyzed using the socioecological framework. Findings demonstrate the positive influences of supportive social networks, perceived benefits, HIV-related knowledge, raising HIV awareness in communities, and impact of HIV state laws. The highlighted barriers were mainly low income, substance use, HIV-related stigma, influence of stereotypes and assumptions about older women living with HIV, and health insurance. Religion, managing comorbidities, attitude toward, HIV disclosure, and caregiving roles had both positive and negative influences on engagement. These findings illuminate factors of HIV treatment engagement that might be culturally founded; disseminating these factors to health care professionals is a critical intervention to support this population.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.135
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.041
GPT teacher head0.398
Teacher spread0.357 · 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 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

Citations13
Published2022
Admission routes1
Has abstractyes

Explore more

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