Literature DB >> 33410755

Content-Based Recommender Support System for Counselors in a Suicide Prevention Chat Helpline: Design and Evaluation Study.

Salim Salmi1, Saskia Mérelle2, Renske Gilissen2, Willem-Paul Brinkman3.   

Abstract

BACKGROUND: The working environment of a suicide prevention helpline requires high emotional and cognitive awareness from chat counselors. A shared opinion among counselors is that as a chat conversation becomes more difficult, it takes more effort and a longer amount of time to compose a response, which, in turn, can lead to writer's block.
OBJECTIVE: This study evaluates and then designs supportive technology to determine if a support system that provides inspiration can help counselors resolve writer's block when they encounter difficult situations in chats with help-seekers.
METHODS: A content-based recommender system with sentence embedding was used to search a chat corpus for similar chat situations. The system showed a counselor the most similar parts of former chat conversations so that the counselor would be able to use approaches previously taken by their colleagues as inspiration. In a within-subject experiment, counselors' chat replies when confronted with a difficult situation were analyzed to determine if experts could see a noticeable difference in chat replies that were obtained in 3 conditions: (1) with the help of the support system, (2) with written advice from a senior counselor, or (3) when receiving no help. In addition, the system's utility and usability were measured, and the validity of the algorithm was examined.
RESULTS: A total of 24 counselors used a prototype of the support system; the results showed that, by reading chat replies, experts were able to significantly predict if counselors had received help from the support system or from a senior counselor (P=.004). Counselors scored the information they received from a senior counselor (M=1.46, SD 1.91) as significantly more helpful than the information received from the support system or when no help was given at all (M=-0.21, SD 2.26). Finally, compared with randomly selected former chat conversations, counselors rated the ones identified by the content-based recommendation system as significantly more similar to their current chats (β=.30, P<.001).
CONCLUSIONS: Support given to counselors influenced how they responded in difficult conversations. However, the higher utility scores given for the advice from senior counselors seem to indicate that specific actionable instructions are preferred. We expect that these findings will be beneficial for developing a system that can use similar chat situations to generate advice in a descriptive style, hence helping counselors through writer's block. ©Salim Salmi, Saskia Mérelle, Renske Gilissen, Willem-Paul Brinkman. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 07.01.2021.

Entities:  

Keywords:  chat corpus; content based recommender system; crisis line; mental health; sentence embedding; suicide; suicide prevention

Mesh:

Year:  2021        PMID: 33410755      PMCID: PMC7819775          DOI: 10.2196/21690

Source DB:  PubMed          Journal:  J Med Internet Res        ISSN: 1438-8871            Impact factor:   5.428


  6 in total

1.  An evaluation of crisis hotline outcomes. Part 2: Suicidal callers.

Authors:  Madelyn S Gould; John Kalafat; Jimmie Lou Harrismunfakh; Marjorie Kleinman
Journal:  Suicide Life Threat Behav       Date:  2007-06

2.  Evaluation of the 113Online Suicide Prevention Crisis Chat Service: Outcomes, Helper Behaviors and Comparison to Telephone Hotlines.

Authors:  Jan K Mokkenstorm; Merijn Eikelenboom; Annemiek Huisman; Jasper Wiebenga; Renske Gilissen; Ad J F M Kerkhof; Johannes H Smit
Journal:  Suicide Life Threat Behav       Date:  2016-08-19

3.  Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications.

Authors:  Titipat Achakulvisut; Daniel E Acuna; Tulakan Ruangrong; Konrad Kording
Journal:  PLoS One       Date:  2016-07-06       Impact factor: 3.240

4.  Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health.

Authors:  Tim Althoff; Kevin Clark; Jure Leskovec
Journal:  Trans Assoc Comput Linguist       Date:  2016

Review 5.  Natural Language Processing of Social Media as Screening for Suicide Risk.

Authors:  Glen Coppersmith; Ryan Leary; Patrick Crutchley; Alex Fine
Journal:  Biomed Inform Insights       Date:  2018-08-27

6.  Content-Based Recommender Support System for Counselors in a Suicide Prevention Chat Helpline: Design and Evaluation Study.

Authors:  Salim Salmi; Saskia Mérelle; Renske Gilissen; Willem-Paul Brinkman
Journal:  J Med Internet Res       Date:  2021-01-07       Impact factor: 5.428

  6 in total
  2 in total

1.  Developing a WhatsApp hotline for female entertainment workers in Cambodia: a qualitative study.

Authors:  Carinne Brody; Rebecca Reno; Pheak Chhoun; Sopherean Ith; Sovanvorleak Tep; Sovannary Tuot; Siyan Yi
Journal:  Mhealth       Date:  2022-01-20

2.  Content-Based Recommender Support System for Counselors in a Suicide Prevention Chat Helpline: Design and Evaluation Study.

Authors:  Salim Salmi; Saskia Mérelle; Renske Gilissen; Willem-Paul Brinkman
Journal:  J Med Internet Res       Date:  2021-01-07       Impact factor: 5.428

  2 in total

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