Evidence map›Paper›PMID 38815258›Full record

Observational studyJournal of medical Internet research2024

Tracking and Profiling Repeated Users Over Time in Text-Based Counseling: Longitudinal Observational Study With Hierarchical Clustering.

Yucan Xu, Christian Shaunlyn Chan, Evangeline Chan, Junyou Chen, Florence Cheung, Zhongzhi Xu, Joyce Liu, Paul Siu Fai Yip

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

8 authors.

Yucan XuSchool of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0003-0247-0404
Christian Shaunlyn ChanDepartment of Psychology, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0003-2616-5199
Evangeline ChanDepartment of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0009-0001-5562-6735
Junyou ChenThe Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0003-2631-6030
Florence CheungThe Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0009-0001-1744-3997
Zhongzhi XuSchool of Public Health, Sun Yat-sen University, Guang Zhou, China.ORCID 0000-0002-0514-0200
Joyce LiuThe Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0009-0008-0018-6219
Paul Siu Fai YipDepartment of Social Work and Social Administration, The University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0003-1596-4120

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDue to their accessibility and anonymity, web-based counseling services are expanding at an unprecedented rate. One of the most prominent challenges such services face is repeated users, who represent a small fraction of total users but consume significant resources by continually returning to the system and reiterating the same narrative and issues. A deeper understanding of repeated users and tailoring interventions may help improve service efficiency and effectiveness. Previous studies on repeated users were mainly on telephone counseling, and the classification of repeated users tended to be arbitrary and failed to capture the heterogeneity in this group of users.

objectiveIn this study, we aimed to develop a systematic method to profile repeated users and to understand what drives their use of the service. By doing so, we aimed to provide insight and practical implications that can inform the provision of service catering to different types of users and improve service effectiveness.

methodsWe extracted session data from 29,400 users from a free 24/7 web-based counseling service from 2018 to 2021. To systematically investigate the heterogeneity of repeated users, hierarchical clustering was used to classify the users based on 3 indicators of service use behaviors, including the duration of their user journey, use frequency, and intensity. We then compared the psychological profile of the identified subgroups including their suicide risks and primary concerns to gain insights into the factors driving their patterns of service use.

resultsThree clusters of repeated users with clear psychological profiles were detected: episodic, intermittent, and persistent-intensive users. Generally, compared with one-time users, repeated users showed higher suicide risks and more complicated backgrounds, including more severe presenting issues such as suicide or self-harm, bullying, and addictive behaviors. Higher frequency and intensity of service use were also associated with elevated suicide risk levels and a higher proportion of users citing mental disorders as their primary concerns.

conclusionsThis study presents a systematic method of identifying and classifying repeated users in web-based counseling services. The proposed bottom-up clustering method identified 3 subgroups of repeated users with distinct service behaviors and psychological profiles. The findings can facilitate frontline personnel in delivering more efficient interventions and the proposed method can also be meaningful to a wider range of services in improving service provision, resource allocation, and service effectiveness.

Indexed as

CounselingAdultCluster AnalysisFemaleHumansLongitudinal StudiesMaleMiddle AgedText MessagingYoung Adultfrequent usershierarchical clusteringpsycholinguistic analysispsychological profilesrepeated usersrisk profilingservice effectivenesstext-based counselingweb-based counseling

Identifiers

PMID38815258
PMCPMC11176871

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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.