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Data and analysis for the publication: Content-Based Recommender Support System for Counselors in a Suicide Prevention Chat Helpline: Design and Evaluation Study

dataset
posted on 12.01.2021, 12:39 by Salim Salmi, Saskia Mérelle, Renske Gilissen, Willem-Paul Brinkman
This dataset contains all data and analysis scripts to the research conducted for the JMIR paper: "Content-based Recommender Support System for Counselors in a Suicide Prevention Chat Helpline: Design and Evaluation Study"

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.


History

Publisher

4TU.ResearchData

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Interactive Intelligence 113 Zelfmoordpreventie

Licence

Exports