Evidence map›Paper›PMID 37253196›Full record

ArticleAIDS care2024

A necessary conversation to develop chatbots for HIV studies: qualitative findings from research staff, community advisory board members, and study participants.

W Scott Comulada, Roxana Rezai, Stephanie Sumstine, Dalmacio Dennis Flores, Tara Kerin, Manuel A Ocasio, Dallas Swendeman, M Isabel Fernández, Adolescent Trials Network (ATN) CARES Team

Abstract read
In one paragraph

Article in AIDS care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

9 authors.

W Scott ComuladaDepartment of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.ORCID 0000-0002-1340-6371
Roxana RezaiDepartment of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA.ORCID 0000-0002-5614-9483
Stephanie SumstineDepartment of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Dalmacio Dennis FloresSchool of Nursing, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-9202-7443
Tara KerinDepartment of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Manuel A OcasioDepartment of Pediatrics, School of Medicine, Tulane University, New Orleans, LO, USA.
Dallas SwendemanDepartment of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
M Isabel FernándezCollege of Osteopathic Medicine, Nova Southeastern University, Miami, FL, USA.
Adolescent Trials Network (ATN) CARES TeamDepartment of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.

Funding

UCLA Rapid, Relevant, Rigorous Implementation Science HubP30MH058107 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Sung-Jae Lee · 1997 to 2026
$53.5M
Optimizing the HIV Treatment Continuum with a Stepped Care Model for Youth Living with HIVU19HD089886 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FERNANDEZ, MARIA ISABEL, ROTHERAM-BORUS, MARY JANE · 2016 to 2022
$23.4M
NICHD NIH HHS U19 HD089886NIMH NIH HHS P30 MH058107
6 · The paper itself

Abstract

Chatbots increase business productivity by handling customer conversations instead of human agents. Similar rationale applies to use chatbots in the healthcare sector, especially for health coaches who converse with clients. Chatbots are nascent in healthcare. Study findings have been mixed in terms of engagement and their impact on outcomes. Questions remain as to chatbot acceptability with coaches and other providers; studies have focused on clients.To clarify perceived benefits of chatbots in HIV interventions we conducted virtual focus groups with 13 research staff, eight community advisory board members, and seven young adults who were HIV intervention trial participants (clients). Our HIV healthcare context is important. Clients represent a promising age demographic for chatbot uptake. They are a marginalized population warranting consideration to avoid technology that limits healthcare access.Focus group participants expressed the value of chatbots for HIV research staff and clients. Staff discussed how chatbot functions, such as automated appointment scheduling and service referrals, could reduce workloads while clients discussed the after-hours convenience of these functions. Participants also emphasized that chatbots should provide relatable conversation, reliable functionality, and would not be appropriate for all clients. Our findings underscore the need to further examine appropriate chatbot functionality in HIV interventions.

Indexed as

HIV InfectionsCommerceCommunicationFocus GroupsHealth FacilitiesHumansYoung AdultChatbotconversational agentHIVmHealthyouth

Identifiers

PMID37253196
PMCPMC10687304

What OpenQuestion holds

Textmetadata
LicenceTDM
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.