| Literature DB >> 34948802 |
Paul Siu Fai Yip1,2, Wai-Leung Chan3, Christian S Chan4, Lihong He1, Yucan Xu1, Evangeline Chan1, Yui Chi Chau5, Qijin Cheng6, Siu-Hung Cheng7, Florence Cheung1, James Chow1, Shirley Chow3, Jerry Fung1, Siu-Man Hsu8, Yik Wa Law2, Billie Lo9, Sze-Man Miu10, Wai Man Ng3, Ken Ngai11, Christy Tsang1, Cynthia Xiong1, Zhongzhi Xu1.
Abstract
We present the opportunities and challenges of Open Up, a free, 24/7 online text-based counselling service to support youth in Hong Kong. The number of youths served more than doubled within the first three years since its inception in 2018 in response to increasing youth suicidality and mental health needs. Good practice models are being developed in order to sustain and further scale up the service. We discuss the structure of the operation, usage pattern and its effectiveness, the use of AI to improve users experience, and the role of volunteer in the operation. We also present the challenges in further enhancing the operation, calling for more research, especially on the identification of the optimal number of users that can be concurrently served by a counsellor, the effective approach to respond to a small percentage of repeated users who has taken up a disproportional volume of service, and the way to optimize the use of big data analytics and AI technology to enhance the service. These advancements will benefit not only Open Up but also similar services across the globe.Entities:
Keywords: Artificial Intelligence; crisis intervention; online emotional support service; online text-based counselling; youth suicide prevention
Mesh:
Year: 2021 PMID: 34948802 PMCID: PMC8701729 DOI: 10.3390/ijerph182413194
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1Open Up’s operation architecture and governance infrastructure.
Figure 2Number of valid cases by 7-day moving average from October 2018 to June 2021.
Risk level changes during the intervention (n = 81,654).
| Highest Risk Level | Exit Risk Level | |||
|---|---|---|---|---|
| Low | Med | High | Crisis | |
| Crisis (1687) | 681 (40.4%) | 713 (42.3%) | 110 (6.5%) | 183 (10.8%) |
| High (1210) | 397 (32.8%) | 571 (47.2%) | 242 (20.0%) | 0 |
| Med (12,103) | 6122 (50.6%) | 5981 (49.4%) | 0 | 0 |
| Low (66,654) | 66,654 (100.0%) | 0 | 0 | 0 |
Percentage of users and cases by number of valid visits in the study period.
| Valid Visits | User Percentage | Case Percentage |
|---|---|---|
| 1 time | 65.5% | 23.6% |
| 2 times | 16.1% | 11.6% |
| 3–5 times | 12.0% | 15.5% |
| 6–10 times | 3.3% | 8.8% |
| 11–20 times | 1.6% | 8.0% |
| 21–50 times | 1.0% | 11.2% |
| over 50 times | 0.5% | 21.3% |
Note: Identified by unique identifiers: (1) IP addresses via web portal; (2) unique number via SMS, Facebook Messenger and WhatsApp.
Figure 3The topic distribution of Open Up users from Oct 2018 to June 2021. Note: The module is under improvement with machine learning.