The PrEP You Want: A Web-Based Survey of Online Cross-Border Shopping for HIV Prophylaxis Medications
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».