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Record W4402992619 · doi:10.2196/58531

Methodology for the Positive Voices 2022 Survey of People With HIV Accessing Care in England, Wales, and Scotland: Cross-Sectional Questionnaire Study

2024· article· en· W4402992619 on OpenAlexvenueno aff
Janey Sewell, Carole Kelly, Adamma Aghaizu, Hannah Kitt, Annegret Pelchen–Matthews, Véronique Martin, Amal Farah, Colette Smith, Alison Brown, Clare Humphreys, Alex Sparrowhawk, Valérie Delpech, Alison Rodger, Fiona Lampe, Meaghan Kall

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyPreprintHuman immunodeficiency virus (HIV)MedicineFamily medicineOptometryWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Due to advances in treatment, HIV is now a chronic condition with near-normal life expectancy. However, people with HIV continue to have a higher burden of mental and physical health conditions and are impacted by wider socioeconomic issues. Positive Voices is a nationally representative series of surveys of people with HIV in the United Kingdom. It monitors the physical, mental, and social health, well-being, and needs of this population so that they can be addressed. OBJECTIVE: This paper aimed to describe the methodology, recruitment strategies, and key demographic features of participants recruited for the second national round of Positive Voices (PV2022). METHODS: PV2022 was a national, cross-sectional questionnaire study that included people attending HIV care at 101 of the 178 clinics in the United Kingdom between April 2022 and March 2023. Data from the HIV and AIDS reporting system (HARS), a national surveillance database of people with HIV and attending care that is held at the UK Health Security Agency (UKHSA), was used as a sampling frame. The information collected in PV2022 included demographic and socioeconomic factors, HIV diagnoses and treatment, mental and physical health, health service use and satisfaction, social care and support, met and unmet needs, stigma and discrimination, quality of life, lifestyle factors, and additional challenges experienced due to the COVID-19 pandemic. Data linkage to HARS enabled the extraction of clinical information on antiretroviral therapy (ART), HIV viral load, and CD4 lymphocyte counts. Probabilistic sampling was used to provide a randomly selected, representative sample of people attending HIV care who could be invited to complete a paper or online questionnaire. At the start of 2023, due to under-recruitment mainly due to the impact of the monkeypox (Mpox) outbreak, a separate sequential recruitment strategy was initiated in 14 of the largest clinics to increase participant numbers. RESULTS: Of the 4622 participants who completed the questionnaire, 3692 were recruited through probabilistic recruitment and 930 through sequential recruitment. The overall response rate (measured as the number of people who completed a questionnaire of those who either accepted or declined) was 50%. Survey respondents represented approximately 1 in 20 people diagnosed with HIV in England, Wales, and Scotland. The median age of participants was 52 years, 3428 of participants were men, 2991 were White, and 1121 were Black. CONCLUSIONS: PV2022 is currently the largest survey of people with HIV in the United Kingdom (as of September 2024). The PV2022 findings will be used to explore the health and well-being of the HIV population and examine associations with demographic, socioeconomic, lifestyle, and other HIV-related factors. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/58531.

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)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.076
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.286
GPT teacher head0.603
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreMethods

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

Citations2
Published2024
Admission routes1
Has abstractyes

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