Abstract
The objective of this study was to understand the attitudes of professionals who work in mental health regarding the use of conversational user interfaces, or chatbots, to support people’s mental health and wellbeing. This study involves an online survey to measure the awareness and attitudes of mental healthcare professionals and experts. The findings from this survey show that over half of the participants in the survey agreed that there are benefits associated with mental healthcare chatbots (65\%, p<0.01). The perceived importance of chatbots was also relatively high (74\%, p<0.01) with more than three quarters (79\%, p<0.01) of respondents agreeing that mental healthcare chatbots could help their clients to better manage their own health, yet chatbots are overwhelmingly perceived as not adequately understanding or displaying human emotion (86\%, p<0.01). Even though the level of personal experience with chatbots among professionals and experts in mental health has been quite low, this study shows that, where they have been used, the experience has been mostly satisfactory. This study has found that, as years of experience increased, there was a corresponding increase in the belief that healthcare chatbots could help clients better manage their own mental health.
| Original language | English |
|---|---|
| Article number | 25 |
| Pages (from-to) | 1-15 |
| Number of pages | 15 |
| Journal | ACM Transactions on Computing for Healthcare |
| Volume | 2 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published (in print/issue) - 15 Jul 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
-
SDG 4 Quality Education
Keywords
- chatbots
- mental healthcare professionals
- conversational user interfaces
- mental health
- mental health survey
Fingerprint
Dive into the research topics of 'Can Chatbots Help Support a Person’s Mental Health? Perceptions and Views from Mental Healthcare Professionals and Experts'. Together they form a unique fingerprint.Prizes
-
Best Journal Paper Award - ACM Transactions on Computing for Healthcare
Sweeney, C. (Recipient), Potts, C. (Recipient), Ennis, E. (Recipient), Bond, R. (Recipient), Mulvenna, M. (Recipient), O'Neill, S. (Recipient), Malcolm, M. (Recipient), Kuosmanen, L. (Recipient), Kostenius, C. (Recipient), Vakaloudis, A. (Recipient), McConvey, G. (Recipient), Turkington, R. (Recipient), Hanna, D. (Recipient), Nieminen, H. (Recipient), Vartiainen, A.-K. (Recipient), Robertson, A. (Recipient) & McTear, M. (Recipient), 8 Jan 2025
Prize
File
Student theses
-
Machine learning of anonymous call data from national suicide prevention helpline services: understanding caller behaviour and policy implications
Turkington, R. (Author), Ennis, E. (Supervisor), O'Neill, S. (Supervisor), Mulvenna, M. (Supervisor) & Bond, R. (Supervisor), Oct 2021Student thesis: Doctoral Thesis
File -
Using data analysis and machine learning to derive insights from text-based mental health and wellbeing digital media
Sweeney, C. (Author), Ennis, E. (Supervisor), Bond, R. (Supervisor) & Mulvenna, M. (Supervisor), Mar 2025Student thesis: Doctoral Thesis
File
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver