Evidence map›Paper›PMID 35930334›Full record

ArticleJMIR human factors2022

Consumer Perspectives on the Use of Artificial Intelligence Technology and Automation in Crisis Support Services: Mixed Methods Study.

Jennifer S Ma, Megan O'Riordan, Kelly Mazzer, Philip J Batterham, Sally Bradford, Kairi Kõlves, Nickolai Titov, Britt Klein, Debra J Rickwood

Abstract read
In one paragraph

Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. 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.

Jennifer S MaDiscipline of Psychology, Faculty of Health, University of Canberra, ACT, Australia.ORCID https://orcid.org/0000-0002-3838-3583
Megan O'RiordanDiscipline of Psychology, Faculty of Health, University of Canberra, ACT, Australia.ORCID https://orcid.org/0000-0002-6700-8029
Kelly MazzerDiscipline of Psychology, Faculty of Health, University of Canberra, ACT, Australia.ORCID https://orcid.org/0000-0001-9694-0758
Philip J BatterhamCentre for Mental Health Research, National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australia.ORCID https://orcid.org/0000-0002-4547-6876
Sally BradfordDepartment of Veteran Affairs, Australian Government, Canberra, Australia.ORCID https://orcid.org/0000-0001-5430-8172
Kairi KõlvesAustralian Institute for Suicide Research and Prevention, School of Applied Psychology, Griffith University, Brisbane, Australia.ORCID https://orcid.org/0000-0002-1638-8981
Nickolai TitovMindSpot and School of Psychology, Macquarie University, Sydney, Australia.ORCID https://orcid.org/0000-0002-7268-729X
Britt KleinHealth Innovation and Transformation Centre, Federation University Australia, Churchill, Australia.ORCID https://orcid.org/0000-0003-2912-8043
Debra J RickwoodDiscipline of Psychology, Faculty of Health, University of Canberra, ACT, Australia.ORCID https://orcid.org/0000-0002-4227-0231

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEmerging technologies, such as artificial intelligence (AI), have the potential to enhance service responsiveness and quality, improve reach to underserved groups, and help address the lack of workforce capacity in health and mental health care. However, little research has been conducted on the acceptability of AI, particularly in mental health and crisis support, and how this may inform the development of responsible and responsive innovation in the area.

objectiveThis study aims to explore the level of support for the use of technology and automation, such as AI, in Lifeline's crisis support services in Australia; the likelihood of service use if technology and automation were implemented; the impact of demographic characteristics on the level of support and likelihood of service use; and reasons for not using Lifeline's crisis support services if technology and automation were implemented in the future.

methodsA mixed methods study involving a computer-assisted telephone interview and a web-based survey was undertaken from 2019 to 2020 to explore expectations and anticipated outcomes of Lifeline's crisis support services in a nationally representative community sample (n=1300) and a Lifeline help-seeker sample (n=553). Participants were aged between 18 and 93 years. Quantitative descriptive analysis, binary logistic regression models, and qualitative thematic analysis were conducted to address the research objectives.

resultsOne-third of the community and help-seeker participants did not support the collection of information about service users through technology and automation (ie, via AI), and approximately half of the participants reported that they would be less likely to use the service if automation was introduced. Significant demographic differences were observed between the community and help-seeker samples. Of the demographics, only older age predicted being less likely to endorse technology and automation to tailor Lifeline's crisis support service and use such services (odds ratio 1.48-1.66, 99% CI 1.03-2.38; P<.001 to P=.005). The most common reason for reluctance, reported by both samples, was that respondents wanted to speak to a real person, assuming that human counselors would be replaced by automated robots or machine services.

conclusionsAlthough Lifeline plans to always have a real person providing crisis support, help-seekers automatically fear this will not be the case if new technology and automation such as AI are introduced. Consequently, incorporating innovative use of technology to improve help-seeker outcomes in such services will require careful messaging and assurance that the human connection will continue.

Indexed as

acceptabilityartificial intelligencecommunityconsumercrisishelp-seekerperspectivesupporttechnology

Identifiers

PMID35930334
PMCPMC9391967

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.