Feasibility of a Population Based Survey on HIV Prevalence in Barbados, and Population Preference for Sample Identification Method
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".