Evidence map›Paper›PMID 37294610›Full record

ArticleJMIR cancer2023

Therapist Feedback and Implications on Adoption of an Artificial Intelligence-Based Co-Facilitator for Online Cancer Support Groups: Mixed Methods Single-Arm Usability Study.

Yvonne W Leung, Steve Ng, Lauren Duan, Claire Lam, Kenith Chan, Mathew Gancarz, Heather Rennie, Lianne Trachtenberg, Kai P Chan, Achini Adikari and 5 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
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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

15 authors.

Yvonne W Leungde Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-3348-1547
Steve Ngde Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-0564-4943
Lauren Duande Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-5849-0837
Claire Lamde Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-6201-0245
Kenith ChanDepartment of Psychology, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-8200-0060
Mathew Gancarzde Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-7922-057X
Heather Renniede Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-1907-8698
Lianne Trachtenbergde Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-6913-7318
Kai P Chande Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-2727-2527
Achini AdikariCentre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.ORCID https://orcid.org/0000-0001-5112-5063
Lin FangFactor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-7944-1882
David GratzerDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-4578-0050
Graeme HirstDepartment of Computer Science, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-9482-1042
Jiahui Wongde Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-0369-2455
Mary Jane EsplenDepartment of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-6034-2235

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe recent onset of the COVID-19 pandemic and the social distancing requirement have created an increased demand for virtual support programs. Advances in artificial intelligence (AI) may offer novel solutions to management challenges such as the lack of emotional connections within virtual group interventions. Using typed text from online support groups, AI can help identify the potential risk of mental health concerns, alert group facilitator(s), and automatically recommend tailored resources while monitoring patient outcomes.

objectiveThe aim of this mixed methods, single-arm study was to evaluate the feasibility, acceptability, validity, and reliability of an AI-based co-facilitator (AICF) among CancerChatCanada therapists and participants to monitor online support group participants' distress through a real-time analysis of texts posted during the support group sessions. Specifically, AICF (1) generated participant profiles with discussion topic summaries and emotion trajectories for each session, (2) identified participant(s) at risk for increased emotional distress and alerted the therapist for follow-up, and (3) automatically suggested tailored recommendations based on participant needs. Online support group participants consisted of patients with various types of cancer, and the therapists were clinically trained social workers.

methodsOur study reports on the mixed methods evaluation of AICF, including therapists' opinions as well as quantitative measures. AICF's ability to detect distress was evaluated by the patient's real-time emoji check-in, the Linguistic Inquiry and Word Count software, and the Impact of Event Scale-Revised.

resultsAlthough quantitative results showed only some validity of AICF's ability in detecting distress, the qualitative results showed that AICF was able to detect real-time issues that are amenable to treatment, thus allowing therapists to be more proactive in supporting every group member on an individual basis. However, therapists are concerned about the ethical liability of AICF's distress detection function.

conclusionsFuture works will look into wearable sensors and facial cues by using videoconferencing to overcome the barriers associated with text-based online support groups. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21453.

Indexed as

canceremotion analysislegal implications of AILIWCnatural language processingrecommender systemtherapist adoptiontherapist attitudestherapist liability

Identifiers

PMID37294610
PMCPMC10334721

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.