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Record W1743619605 · doi:10.2174/1874613601004010084

Feasibility of a Population Based Survey on HIV Prevalence in Barbados, and Population Preference for Sample Identification Method

2010· article· en· W1743619605 on OpenAlexaff
O Peter Adams, Anne O. Carter

Bibliographic record

VenueThe Open AIDS Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineCondomPopulationDemographyHuman immunodeficiency virus (HIV)Family medicinePrevalenceChlamydiaSample (material)Environmental healthImmunology

Abstract

fetched live from OpenAlex

OBJECTIVE: To predict response rate and validity of a population-based HIV prevalence survey in Barbados using oral fluid, and the method of sample identification preferred by participants. METHODS: Persons age 18 to 35 randomly selected from the voters' register to participate in a study of the prevalence of chlamydia and gonorrhoea (STI) were invited to answer a questionnaire. RESULTS: Of 496 persons selected for the STI study, 88 did not participate, and a further 10 did not answer the questionnaire, leaving 398 respondents. 329 persons or 66% (60% men, 73% women, p = 0.003) of the original 496 persons said that they would be willing to take part in an HIV survey using oral fluid. People indicating willingness to take part in an HIV survey did not differ significantly from non-respondents and those indicating unwillingness to participate by a number of demographic and STI risk factors including age, education level, partnership status, number of partners, condom use, drug use, and STI infection status. For persons willing to participate in a HIV survey, confidential linked sample identification was acceptable to 99.0% (95% CI +/- 1.0), and unlinked identification to 1.6% (95% CI +/- 1.3). CONCLUSION: The HIV prevalence estimated by a linked survey would have a reasonable response rate and be valid, as likelihood of participation is not related to infection risk.

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)0.121
metaresearch head score (Gemma)0.049
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1210.049
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.454
GPT teacher head0.529
Teacher spread0.075 · 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; both teacher heads agree on what is shown here.

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
Published2010
Admission routes1
Has abstractyes

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