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Record W4388424654 · doi:10.2196/preprints.54313

Assessing Outcomes in HIV Prevention and Treatment Programs With Female Sex Workers and Men Who Have Sex With Men: Expanded Polling Booth Survey Protocol (Preprint)

2023· preprint· en· W4388424654 on OpenAlexaboutno aff
Parinita Bhattacharjee, Leigh M. McClarty, Joshua Kimani, Shajy Isac, Rhoda Kabuti, A. M. Kinyua, Jaffred Karakaja Okoyana, Virjinia Njeri Ndukuyu, Helgar Musyoki, Anthony Kiplagat, Peter Arimi, Souradet Y. Shaw, Faran Emmanuel, Monica Gandhi, Marissa Becker, James Blanchard

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHIV/AIDS Impact and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsData collectionPollingPopulationFocus groupGovernment (linguistics)Qualitative propertyHuman immunodeficiency virus (HIV)MedicinePsychologyDemographyEnvironmental healthFamily medicineSociologyComputer science

Abstract

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<sec> <title>BACKGROUND</title> Assessing HIV outcomes in key population prevention programs is a crucial component of the program cycle, as it facilitates improved planning and monitoring of anticipated results. The Joint United Nations Programme on HIV and AIDS recommends using simple, rapid methods to routinely measure granular and differentiated program outcomes for key populations. Following a program science approach, Partners for Health and Development in Africa, in partnership with the Nairobi County Government and the University of Manitoba, aims to conduct an outcome assessment using a novel, expanded polling booth survey (ePBS) method with female sex workers and men who have sex with men in Nairobi County, Kenya. </sec> <sec> <title>OBJECTIVE</title> This study aims to (1) estimate the incidence and prevalence of HIV; (2) assess biomedical, behavioral, and structural outcomes; and (3) understand barriers contributing to gaps in access and use of available prevention and treatment services among female sex workers and men who have sex with men in Nairobi. </sec> <sec> <title>METHODS</title> The novel ePBS approach employs complementary data collection methods, expanding upon the traditional polling booth survey (PBS) method by incorporating additional quantitative, qualitative, and biological data collection components and an improved sampling methodology. Quantitative methods will include (1) PBS, a group interview method in which individuals provide responses through a ballot box in an unlinked and anonymous way, and (2) a behavioral and biological survey (BBS), including a face-to-face individual interview and collection of linked biological samples. Qualitative methods will include focus group discussions. The ePBS study uses a 2-stage, population- and location-based random sampling approach involving the random selection of locations from which random participants are selected at a predetermined time on a randomly selected day. PBS data will be analyzed at the group level, and BBS data will be analyzed at an individual level. Qualitative data will be analyzed thematically. </sec> <sec> <title>RESULTS</title> Data were collected from April to May 2023. The study has enrolled 759 female sex workers (response rate: 759/769, 98.6%) and 398 men who have sex with men (response rate: 398/420, 94.7%). Data cleaning and analyses are ongoing, with a focus on assessing gaps in program coverage and inequities in program outcomes. </sec> <sec> <title>CONCLUSIONS</title> The study will generate valuable HIV outcome data to inform program improvement and policy development for Nairobi County’s key population HIV prevention program. This study served as a pilot for the novel ePBS method, which combines PBS, BBS, and focus group discussions to enhance its programmatic utility. The ePBS method holds the potential to fill an acknowledged gap for a rapid, low-cost, and simple method to routinely measure HIV outcomes within programs and inform incremental program improvements through embedded learning processes. </sec>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.099
GPT teacher head0.337
Teacher spread0.238 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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