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Record W4295926193 · doi:10.3389/fpubh.2022.916224

Behavioral intervention grounded in motivational interviewing and behavioral economics shows promise with Black and English-speaking Latino persons living with HIV with unsuppressed HIV viral load in New York City: A mixed methods pilot study

2022· article· en· W4295926193 on OpenAlexfundno aff
Marya Gwadz, Samantha Serrano, Sebastian Linnemayr, Charles M. Cleland, Sabrina R. Cluesman, Robin Freeman, Kinsey Kellam, Corey De Stefano, Khadija Israel, Emily Pan

Bibliographic record

VenueFrontiers in Public Health · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute on Drug AbuseYork UniversityCenter for Drug Use and HIV ResearchNew York University
KeywordsMotivational interviewingHuman immunodeficiency virus (HIV)Intervention (counseling)Grounded theoryPsychologyViral loadDevelopmental psychologyClinical psychologyMedicineQualitative researchGerontologyPsychiatryFamily medicineSociology

Abstract

fetched live from OpenAlex

Introduction: Sustained HIV viral suppression is the ultimate goal of HIV treatment. African American/Black and Latino persons with HIV (PWH) in the United States are less likely than their White peers to achieve and sustain viral suppression. To address these disparities, we developed a "low-touch" behavioral intervention drawing on motivational interviewing and behavioral economics. The intervention had three main components: (1) a motivational interviewing counseling session, (2) 16 weeks of automated text messages and quiz questions about HIV management, where participants earned points by answering quiz questions, and 3) a lottery prize, based on viral suppression status, number of points earned, and chance (max. $275). Materials and methods: The intervention was tested in a pre-test/post-test design. The present pilot study used mixed methods to explore the intervention's feasibility, acceptability, impact, and ways it could be improved. Participants engaged in a baseline assessment, qualitative interview, and two structured follow-up assessments over an 8-month period, and provided laboratory reports to document HIV viral load. We carried out descriptive quantitative analyses. Qualitative data were analyzed using a directed content analysis approach. Data integration was carried out using the joint display method. Findings: = 40) were 50 years old, on average (SD = 11), and approximately half (58%) were male. Close to two-thirds (68%) were African American/Black and 32% were Latino. Participants were diagnosed with HIV 22 years ago on average (SD = 8). The intervention was feasible (e.g., mean number of quiz questions answered = 13/16) and highly acceptable. While not powered to assess efficacy, the proportion with suppressed HIV viral load increased from baseline to follow-up (46% participants at the first, 52% participants at the second follow-up evidenced HIV viral suppression). In qualitative analyses, perspectives included that overall, the intervention was acceptable and useful, it was distinct from other programs, lottery prizes were interesting and appreciated but not sufficient to motivate behavior change, and the structure of lottery prizes was not sufficiently clear. Regarding data integration, qualitative data shed light on and extended quantitative results, and added richness and context. Conclusion: This low-touch intervention approach is sufficiently promising to warrant refinement and study in future research.

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)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.368
Teacher spread0.266 · 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 designNon-randomized trial
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

Citations10
Published2022
Admission routes1
Has abstractyes

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