Location location location: an exploration of disparities in access to publicly listed pre-exposure prophylaxis clinics in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: HIV pre-exposure prophylaxis (PrEP) is highly effective in preventing HIV transmission. Finding a PrEP provider, however, can be a barrier to accessing care. This study explores the distribution of publicly listed PrEP-providing clinics in the United States. METHODS: Data regarding 2094 PrEP-providing clinics come from PrEP Locator, a national database of PrEP-providing clinics. We compared the distribution of these PrEP clinics to the distribution of new HIV diagnoses within various geographical areas and by key populations. RESULTS: Most (43/50) states had less than one PrEP-providing clinic per 100,000 population. Among states, the median was two clinics per 1000 PrEP-eligible men who have sex with men. Differences between disease burden and service provision were seen for counties with higher proportions of their residents living in poverty, lacking health insurance, identifying as African American, or identifying as Hispanic/Latino. The Southern region accounted for over half of all new HIV diagnoses but only one-quarter of PrEP-providing clinics. CONCLUSIONS: The current number of PrEP-providing clinics is not sufficient to meet needs. In addition, PrEP-providing clinics are unevenly distributed compared to disease burden, with poor coverage in the Southern divisions and areas with higher poverty, uninsured, and larger minority populations. PrEP services should be expanded and targeted to address disparities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it