Evidence map›Paper›PMID 35513743›Full record

ArticleThe journal of behavioral health services & research2022

Potential Role of Conversational Agents in Encouraging PrEP Uptake.

Maryam Hassani, Sean D Young

Open access · bronzeAbstract read
In one paragraph

Article in The journal of behavioral health services & research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 37% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Maryam HassaniDepartment of Informatics, University of California, Irvine, 6091 Bren Hall, Irvine, CA, 92617, USA.
Sean D YoungDepartment of Informatics, University of California, Irvine, 6091 Bren Hall, Irvine, CA, 92617, USA. Syoung5@hs.uci.edu.ORCID http://orcid.org/0000-0001-6052-4875
University of California, Irvine · US

Funding

Adapting the HOPE Online Support Intervention to Increase MAT Uptake Among OUD PatientsR33AT010606 · NCCIH · UNIVERSITY OF CALIFORNIA-IRVINE · PI YOUNG, SEAN · 2020 to 2021
$3.5M
Mining real-time social media big data to monitor HIV: Development and Ethical IssuesR01AI132030 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI YOUNG, SEAN · 2017 to 2020
$3.4M
HOPE Social Media Intervention for HIV Testing and Studying Social NetworksR01MH106415 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI YOUNG, SEAN · 2015 to 2019
$2.9M
Digital Device ID Targeting for Increasing Medications for Opioid Use Disorder: A Feasibility and Acceptability StudyR34DA054511 · NIDA · UNIVERSITY OF CALIFORNIA-IRVINE · PI YOUNG, SEAN · 2021 to 2023
$707k
NCCIH NIH HHS R33 AT010606NIAID NIH HHS R01 AI132030NIDA NIH HHS R34 DA054511NIMH NIH HHS MH106415NIMH NIH HHS R01 MH106415
6 · The paper itself

Abstract

Approximately 1.2 million people are living with HIV, with many of them unaware of their infection. Pre-exposure prophylaxis (PrEP) is available to minimize transmission among those at high risk for infection, including men who have sex with men, people who inject drugs, and female sex workers. Despite its availability, there is low usage of PrEP. To address this problem, various digital tools have been examined in HIV research. Among those, conversational agents are still underused and their capacity warrants examination to reach at-risk populations. In this paper, we discuss the potential of conversational agents in increasing uptake of PrEP by addressing barriers experienced among those at high risk for infection.

Identifiers

PMID35513743
PMCPMC9071246
OpenAlexW4229008562

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.