Patient perspectives on opt-out HIV screening in a Guyanese emergency department
Bibliographic record
Abstract
In 2007, the WHO recommended that healthcare providers in areas of a generalised HIV epidemic perform HIV testing on all adults and adolescents presenting for healthcare. Studies regarding patient acceptability of opt-out testing, however, have reported wide variation in acceptance rates. This study examines patient-reported acceptability of such testing at the emergency department (ED) of Georgetown Public Hospital Corporation, the largest public hospital in Guyana. In June 2010, a convenience sample of 343 non-critical adult patients who presented to the ED were interviewed regarding potential acceptance of opt-out HIV testing, with 75.5% (95% CI 70.5-80.0%) stating they would accept testing should it be implemented in the ED. Of 12 patient characteristics, 3 had significant differences in acceptance rates on multivariate analysis: age; gender; and previous HIV testing. In this study, potential reasons for declining testing were also examined. The highest percentage of patient agreement was with the statements 'I have had an HIV test recently enough' (84%, 95% CI 74.0-91.4%) and 'I am not at risk for HIV/AIDS' (83%, 95% CI 73.0-90.4%). The results of this study indicate that the majority of patients in this setting would accept opt-out HIV testing, although some still had concerns regarding testing. Opt-out testing in the ED has the potential to facilitate national goals for increased testing and diagnosis.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".