Risk Behavior Disclosure During HIV Test Counseling
Bibliographic record
Abstract
Individualized risk assessments during HIV testing are an integral component of prevention counseling, a currently recommended behavioral intervention for patients in high-risk settings. Additionally, aggregate risk assessment data are the source of aggregate behavioral statistics that inform prevention programs and allocation of resources. Consequently, inaccurate or incomplete risk behavior disclosure during test counseling may impact the efficacy of the counseling intervention, as well as bias aggregate behavioral statistics. To quantify client-reported accuracy during the risk assessment and identify barriers and facilitators to risk behavior disclosure, we interviewed young men accessing HIV testing services in a southeastern United States city using mixed methodology. Data were collected from August 2007 to April 2008. Based on data collected via an audio and computer-assisted self-interview (n = 203), over 30% of men reported that they were not accurate during the risk assessment. Participants reported numerous interpersonal facilitators to complete disclosure. During qualitative interviews (n = 25), participants revealed that many did not understand the purpose of the risk assessment. Findings suggest that risk assessments completed during HIV test counseling may be incomplete. Modifications to the risk assessment process, including better explaining the role of the risk assessment in prevention counseling, may increase the validity of the data.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".