PrEP Implementation Behaviors of Community-Based HIV Testing Staff: A Mixed-Methods Approach Using Latent Class Analysis
Bibliographic record
Abstract
BACKGROUND: Pre-exposure Prophylaxis (PrEP) is an important option for HIV prevention, but the approach has reached a limited number of people at risk of HIV infection. METHODS: A mixed-methods concurrent triangulation design was used to investigate unobserved subgroups of staff who provide community-based, publicly funded HIV testing in Florida (USA). PrEP implementation groups, or classes, were determined using latent class analysis. Generalized linear mixed models were used to estimate PrEP implementation as a function of staff characteristics. In-depth interviews based on the Consolidated Framework for Implementation Research were analyzed thematically. RESULTS: Based on fit statistics and theoretical relevance, a 3-class latent class analysis was selected. Class 1 ("Universal") staff were highly likely to talk about PrEP with their clients, regardless of client eligibility. Class 2 ("Eligibility dependent") staff were most likely to discuss PrEP if they believed their client was eligible. Class 3 ("Limited") staff sometimes spoke to clients about PrEP, but not systematically. In multivariate analyses, only race and sexual orientation remained significant predictors of the PrEP implementation group. Staff who identified as a racial or sexual minority were less likely to be in the Limited group than their heterosexual or white counterparts. Age, gender, ever having taken PrEP, and HIV status did not impact the odds of being in a specific PrEP implementation group. CONCLUSIONS: A subset of HIV testing staff differentially discuss PrEP based on perceived client eligibility; others inconsistently talk to clients about PrEP. Targeted training based on PrEP implementation groups may be beneficial.
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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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".