Online Navigation for Pre-Exposure Prophylaxis via PleasePrEPMe Chat for HIV Prevention: Protocol for a Development and Use Study
Bibliographic record
Abstract
BACKGROUND: Pre-exposure prophylaxis is an HIV medication taken by an individual who is HIV-negative to prevent infection before exposure to the virus. Numerous clinical studies in various communities have shown high rates of effectiveness when pre-exposure prophylaxis is taken as prescribed. Since FDA (US Food and Drug Administration) approval of the first product for pre-exposure prophylaxis in 2012, uptake has been lower than the estimated 1.1 million US adults who could benefit from its use, with an estimated 70,394 individuals on pre-exposure prophylaxis in 2017. Of these, only 11% were Black and 13% were Hispanic despite Black and Hispanic individuals comprising two-thirds of individuals who could benefit, highlighting racial and ethnic disparities in pre-exposure prophylaxis uptake. Patient navigators have been shown to be effective in improving the linkage and retention in care outcomes of people living with HIV across the HIV treatment cascade and can be used throughout the pre-exposure prophylaxis care continuum to assist decision making and connect potential users to pre-exposure prophylaxis services. OBJECTIVE: PleasePrEPMe Chat was designed as a novel online strategy aimed at improving engagement in pre-exposure prophylaxis care services with pre-exposure prophylaxis-eligible populations in California via free HIV-prevention information and health care navigation services. METHODS: Visitors connected with navigators via online bilingual (English, Spanish) chat. During the chat, navigators helped locate pre-exposure prophylaxis services through the PleasePrEPMe provider directory, provided links to HIV-prevention resources, and supported uninsured, insured, and undocumented visitors with benefits navigation. Data such as date, time, type of encounter, visitor type, key demographics, discussion topics, insurance, and other relevant information were collected via a chat log and through the HealthEngage chat platform. RESULTS: From April 2017 to December 2019, PleasePrEPMe completed 2191 online chats. Mean interaction time was 16 minutes, with 68% of chats covering more than one topic. Conversation topics included health care navigation (1104/2191, 50.39%), provider identification (954/2191, 43.54%), pre-exposure prophylaxis information (773/2191, 35.28%), post-exposure prophylaxis information (318/2191, 14.91%), and the California Pre-Exposure Prophylaxis Assistance Program (232/2191, 10.59%). Referrals to pre-exposure prophylaxis- or non pre-exposure prophylaxis-related resources included directory updates, HIV testing and treatment, undetectable=untransmittable, reproductive health, sexually transmitted infections, and other prevention methods. A total of 368 chat visitors completed a voluntary satisfaction scale rating the quality and helpfulness of the service provided, producing a mean rating of 4.7 out of 5. CONCLUSIONS: Online chat is a method for reaching people not already engaged in HIV-prevention services, supporting HIV-prevention decision making, and linking people seeking information online with in-person services. Additional research to evaluate online sexual health information services and understand how social determinants of health influence online engagement is needed to better understand how to reach priority populations not well served by current HIV-prevention services. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/20187.
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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 | 0.043 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.130 | 0.031 |
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".