HIV pre-exposure prophylaxis (PrEP) and treatment as prevention (TasP): What mental health providers should know
Bibliographic record
Abstract
Pharmacologic methods of treating and preventing HIV have advanced tremendously in recent years. Understandings of HIV risk and recommendations for risk-reduction strategies have also changed substantially. A majority of new cases of HIV in many developed countries are now acquired through sex with long-term partners who are unaware of their HIV-positive status, rather than from casual or anonymous sexual encounters. Persons with bipolar disorder and substance use disorders are at particularly high risk. Mental health providers who work with LGBT persons and other populations at higher risk for HIV need to understand strategies their patients are using for HIV risk reduction, and to refer appropriate patients for consideration for pre-exposure prophylaxis (PrEP). PrEP is the daily use of an antiretroviral (ARV) medication for prevention of HIV infection in higher-risk individuals. The United States approved tenofovir + emtracitabine for PrEP in 2012; this is under review in several European countries, Canada, and Australia, and is already prescribed off-label in many. Additionally, studies have shown that treatment with ARV medications to an “undetectable viral load” greatly reduces the risk of further transmission by persons already infected with HIV, called “treatment as prevention” (TasP). As of September 2015, WHO recommends early ARV treatment for all persons with HIV, and consideration of PrEP for men who have sex with men. This paper reviews findings from the PrEP studies (especially iPrEx, iPrEx Ole, IPERGAY, and PROUD) and TasP, and looks at their impact on LGBT and HIV+ communities, with relevance for mental health providers. Disclosure of interest The author has not supplied his declaration of competing interest.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".