Evidence map›Paper›PMID 39037754›Full record

ArticleJMIR cancer2024

Artificial Intelligence-Based Co-Facilitator (AICF) for Detecting and Monitoring Group Cohesion Outcomes in Web-Based Cancer Support Groups: Single-Arm Trial Study.

Yvonne W Leung, Elise Wouterloot, Achini Adikari, Jinny Hong, Veenaajaa Asokan, Lauren Duan, Claire Lam, Carlina Kim, Kai P Chan, Daswin De Silva and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR cancer, 2024. 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
–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

2 citing papers in PubMed.

  1. Review
  2. Article
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

14 authors.

Yvonne W Leungde Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-3348-1547
Elise Wouterlootde Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-3549-9900
Achini AdikariCentre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.ORCID https://orcid.org/0000-0001-5112-5063
Jinny Hongde Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-7562-9140
Veenaajaa Asokande Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-0972-4238
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
Carlina Kimde Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0009-0004-6678-6605
Kai P Chande Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-2727-2527
Daswin De SilvaCentre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia.ORCID https://orcid.org/0000-0003-3878-5969
Lianne Trachtenbergde Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-6913-7318
Heather Renniede Souza Institute, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-1907-8698
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

backgroundCommonly offered as supportive care, therapist-led online support groups (OSGs) are a cost-effective way to provide support to individuals affected by cancer. One important indicator of a successful OSG session is group cohesion; however, monitoring group cohesion can be challenging due to the lack of nonverbal cues and in-person interactions in text-based OSGs. The Artificial Intelligence-based Co-Facilitator (AICF) was designed to contextually identify therapeutic outcomes from conversations and produce real-time analytics.

objectiveThe aim of this study was to develop a method to train and evaluate AICF's capacity to monitor group cohesion.

methodsAICF used a text classification approach to extract the mentions of group cohesion within conversations. A sample of data was annotated by human scorers, which was used as the training data to build the classification model. The annotations were further supported by finding contextually similar group cohesion expressions using word embedding models as well. AICF performance was also compared against the natural language processing software Linguistic Inquiry Word Count (LIWC).

resultsAICF was trained on 80,000 messages obtained from Cancer Chat Canada. We tested AICF on 34,048 messages. Human experts scored 6797 (20%) of the messages to evaluate the ability of AICF to classify group cohesion. Results showed that machine learning algorithms combined with human input could detect group cohesion, a clinically meaningful indicator of effective OSGs. After retraining with human input, AICF reached an F

conclusionsAICF has the potential to assist therapists by detecting discord in the group amenable to real-time intervention. Overall, AICF presents a unique opportunity to strengthen patient-centered care in web-based settings by attending to individual needs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21453.

Indexed as

affectAIartificial intelligencecancercorpusemotionemotion analysisemotion detectionemotion liningfocus groupgroup cohesionintegrating human knowledgelanguagelinguisticLIWCmachine learningnamed entity recognitionnatural language processingNLPoncologyonline support grouponline therapistsentiment analysisspeech tagspeech taggingspoken language processingsupport grouptherapytopic modeling

Identifiers

PMID39037754
PMCPMC11301110

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.