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Record W4401892651 · doi:10.1080/15381501.2024.2385988

“They need people to actually show up in their lives:” deploying community health workers to improve mental health and substance use outcomes among PLWH

2023· article· en· W4401892651 on OpenAlexaff
Phillip Marotta, Chelsey R. Carter, Yue Hu, Victor Wang, Darius Rucker, Johnnie Jones, Greg Gross, Tawnya Brown, Donna Spiegelman, Debbie Humphries

Bibliographic record

VenueJournal of HIV/AIDS & Social Services · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsinVentiv Health Clinical
Fundersnot available
KeywordsMental healthHarm reductionSyndemicSubstance usePsychological interventionHuman immunodeficiency virus (HIV)Qualitative researchMedicineHarmHealth careCommunity health workersSubstance abusePsychologyNursingPsychiatryEnvironmental healthHealth servicesFamily medicinePopulationSociologySocial psychology

Abstract

fetched live from OpenAlex

Mental health and substance use disorders are associated with lower adherence to anti-retroviral treatment, and viral suppression among people living with HIV (PLWH). The first objective of this study was to identify syndemic drivers of mental health problems, substance use and HIV engagement among PLWH. The second aim was to elucidate perspectives on using community health workers (CHWs) as an implementation strategy for increasing engagement in harm reduction services, mental health care and HIV treatment. Key informant interviews analyzed using rapid qualitative analysis identified key themes related to poor MH and substance use as barriers to engagement in HIV care, housing insecurity, the essential role of CHWs in delivering harm reduction services, and other barriers and facilitators. Interventions are needed to evaluate the potential effectiveness of deploying CHWs to enhance coordination of care, trust and the quality of services received by PLWH with mental health and substance use disorders.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.207
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.353
Teacher spread0.317 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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