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Record W4353020482 · doi:10.2196/42888

Patient-Reported Outcomes Collection at an Urban HIV Clinic Associated With a Historically Black Medical College in the Southern United States: Qualitative Interview Study Among Patients With HIV

2023· article· en· W4353020482 on OpenAlexvenueno aff
Paul E Parisot, Facerlyn Wheeler, Kemberlee Bonnet, Peter F. Rebeiro, Cassandra O Schember, Korlu McCainster, Robert L Cooper, Vladimir Berthaud, David G. Schlundt, April C. Pettit

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthCenter for AIDS Research, University of WashingtonHealth Resources and Services AdministrationNational Institutes of Health
KeywordsFeelingMedicineFamily medicineNonprobability samplingQualitative researchHealth careCoding (social sciences)Grounded theoryGerontologyPsychologyPopulationSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Black Americans, particularly in the southern United States, are disproportionately affected by the US HIV epidemic. Patient-reported outcome (PRO) data collection can improve patient outcomes and provide oft-overlooked data on mental health, substance use, and patient adherence to antiretroviral therapy. OBJECTIVE: We piloted the use of an electronic tablet to collect PRO data on social and behavioral determinants of health among people with HIV at the Meharry Community Wellness Center, an HIV clinic affiliated with a Historically Black Medical College in Nashville, Tennessee. Our primary objective was to better understand patients' experiences and comfort with using an electronic PRO tool through patient interviews. METHODS: We enrolled 100 people with HIV in care at the Meharry Community Wellness Center consecutively to completely validate PRO tools using the Research Electronic Data Capture platform on a hand-held tablet. Using a purposive sampling strategy, we enrolled 20 of the 100 participants in an in-depth interview (IDI). Interview guide development was grounded in the cognitive-behavioral model, in which thoughts, feelings, and behaviors are interrelated. IDIs were audio recorded, transcribed, deidentified, and formatted for coding. A hierarchical coding system was developed and refined using an inductive-deductive approach. RESULTS: Among the 100 people with HIV enrolled, the median age was 50 (IQR 42-54) years; 89% (n=89) were Black, 60% (n=60) were male, and 82% (n=82) were living below 100% of the federal poverty level in 2016. Five major interview themes emerged: overall experience, question content, sensitive topics, clinic visit impact, and future recommendations. IDI participants felt that the tablet was easy to use and that the question content was meaningful. Question content related to trauma, sexual and drug use behaviors, mental health, stigma, and discrimination elicited uncomfortable or distressing feelings in some participants. Patients expressed a strong desire to be truthful, and most would complete these surveys without compensation at future visits if offered. CONCLUSIONS: The use of an electronic tablet to complete PRO data collection was well received by this cohort of vulnerable persons in HIV care in the southern United States. Despite some discomfort related to question content, our cohort overwhelmingly believed this was a meaningful part of their medical experience and expressed a high desire for truthfulness. Future research will focus on scaling up the implementation and evaluation of PRO data collection in a contextually appropriate manner while obtaining input from providers and staff to ensure that the collected data are both applicable and actionable.

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)0.011
metaresearch head score (Gemma)0.016
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 score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.094
GPT teacher head0.447
Teacher spread0.354 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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