Feasibility of implementing rapid oral fluid HIV testing in an urban University Dental Clinic: a qualitative study
Bibliographic record
Abstract
BACKGROUND: More than 1 million individuals in the U.S. are infected with HIV; approximately 20% of whom do not know they are infected. Early diagnosis of HIV infection results in earlier access to treatment and reductions in HIV transmission. In 2006, the CDC recommended that health care providers offer routine HIV screening to all adolescent and adult patients, regardless of community seroprevalence or patient lifestyle. Dental providers are uniquely positioned to implement these recommendations using rapid oral fluid HIV screening technology. However, thus far, uptake into dental practice has been very limited. METHODS: The study utilized a qualitative descriptive approach with convenience samples of dental faculty and students. Six in-depth one-on-one interviews were conducted with dental faculty and three focus groups were conducted with fifteen dental students. RESULTS: Results were fairly consistent and indicated relatively high levels of acceptability. Barriers and facilitators of oral fluid HIV screening were identified in four primary areas: scope of practice/practice enhancement, skills/knowledge/training, patient service/patient reactions and logistical issues. CONCLUSIONS: Oral fluid HIV screening was described as having benefits for patients, dental practitioners and the public good. Many of the barriers to implementation that were identified in the study could be addressed through training and interdisciplinary collaborations.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".