The PrEP You Want: A Web-Based Survey of Online Cross-Border Shopping for HIV Prophylaxis Medications
Bibliographic record
Abstract
BACKGROUND: In response to the high cost of HIV pre-exposure prophylaxis (PrEP) medications in Canada, community organizations have created internet-based guides detailing how to legally order generic medications online and travel to collect them in the United States. However, little is known about the patients following these guides. OBJECTIVE: Our primary objective was to measure the proportion of Ontario gay, bisexual, and other men who have sex with men (GBMSM) accessing these online guides who intended to use the border-crossing approach. Our secondary objectives were to explore their demographic characteristics, their completion of the steps in the border-crossing approach, and the barriers they perceived. METHODS: Between July 20, 2017, and May 18, 2018, we administered two online surveys of GBMSM accessing an online border-crossing guide posted by a gay men's health organization in Ontario. Participants completed an open baseline survey posted on the border-crossing guide's Web page and a follow-up survey 3 months later. The data were analyzed using descriptive statistics. We used multivariable logistic regression to identify characteristics associated with the intention to use the border-crossing approach. RESULTS: Most of the 141 participants were young (median age 23, interquartile range 22-25 years) and black (79.4%; 112/141) GBMSM who had completed a college or an undergraduate degree (62.4%; 88/141). In addition, 19.9% (28/141) of them reported a total family income less than Can $30,000 and another 53.9% (76/141) reported income between Can $30,000 and Can $60,000. 54.6% (76/141) paid for medications entirely out of pocket. Most participants indicated that they were likely to complete a border-crossing approach: 80.1% (113/141) at baseline and 79.1% (87/110) at follow-up. The characteristics associated with the intention to use the approach included being black (adjusted odds ratio [aOR] 5.73, 95% CI 2.06-16.61), paying for medications out of pocket (aOR 5.18, 95% CI 1.82-17.04), and having a provider who was thought to be willing to prescribe PrEP (aOR 4.42, 95% CI 1.63-12.41). Comparing baseline and follow-up for the 110 participants who completed both surveys, 65.4% (72/110) and 80.0% (88/110) had discussed PrEP with a health care provider, 18.1% (20/110) and 25.4% (28/110) had obtained a PrEP prescription, and 8.2% (9/110) and 5.5% (6/110) had ordered medications to that mailbox, whereas only 1.0% (1/110) and 0.0% (0/110) had crossed the border to collect them at baseline and follow-up, respectively. Reported barriers included perceived concerns about the approach's legality (56.0%; 79/141), the security of personal health information (39.0%; 55/141), and the safety of online vendors (38.3%; 54/141). CONCLUSIONS: Despite high interest in pursuing an online border-crossing approach to get PrEP medications, such an approach may not be a viable option for PrEP scale-up among interested GBMSM because of logistical challenges and perceptions of safety and legitimacy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".