Online Navigation for Pre-Exposure Prophylaxis via PleasePrEPMe Chat for HIV Prevention: Protocol for a Development and Use Study
Notice bibliographique
Résumé
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.
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 | 0,043 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,130 | 0,031 |
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 ».