ORCID Profile
0000-0001-9050-6520
Current Organisation
University College London
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Publisher: MDPI AG
Date: 08-08-2022
Abstract: Mental health literacy (MHL) promotes mental health among youths. We aimed to evaluate the effectiveness of the newly developed HOPE intervention in improving depression literacy, anxiety literacy, psychological well-being, and reducing personal stigma and stress levels amongst young adults at a university in Singapore. After two pilot studies, we conducted a randomised controlled trial (RCT) and recruited 174 participants aged 18–24 years old through social media platforms. The HOPE intervention group received four online sessions over two weeks and the control group received online inspirational quotes. Study outcomes were measured with self-reported questionnaires and they were assessed at baseline, post-intervention, and two-month follow-up (ClinicalTrials.gov: NCT04266119). Compared with the control arm, the intervention group was associated with increased depression and anxiety literacy levels at post-intervention and two-month follow-up. In addition, personal stigma for depression was reduced at the post-intervention juncture. However, there were no statistically significant changes in the ratings of psychological well-being and stress levels between the two groups. Longitudinal studies with larger s le sizes are warranted to replicate and extend the extant findings.
Publisher: MDPI AG
Date: 04-01-2023
DOI: 10.3390/JPM13010112
Abstract: Interventions adopting augmented and virtual reality (AR/VR) modalities allow participants to explore and experience realistic scenarios, making them useful psycho-educational tools for mental illnesses. This scoping review aims to evaluate the effectiveness of AR/VR interventions in improving (1) knowledge, (2) attitudes, (3) empathy and (4) stigma regarding people with mental illnesses. Literature on published studies in English up till April 2022 was searched within several databases. Sixteen articles were included. The majority of studies were conducted in the West (93.8%), within undergraduates (68.8%) but also amongst high school students, patients, caregivers, public including online community, and covered conditions including psychotic illnesses, dementia, anxiety and depression. A preponderance of these included studies which employed AR/VR based interventions observed improvements in knowledge (66.7%), attitudes (62.5%), empathy (100%) and reduction of stigma (71.4%) pertaining to people with mental illnesses. In the context of relatively limited studies, extant AR/VR based interventions could potentially improve knowledge, attitudes, empathy and decrease stigma regarding people with mental illness. Further research needs to be conducted in larger and more erse s les to investigate the relatively beneficial effects of different AR/VR modalities and the durability of observed improvements of relevant outcomes of interests over time for different mental conditions.
Publisher: Wiley
Date: 04-05-2020
DOI: 10.1111/JAN.14393
Publisher: MDPI AG
Date: 07-09-2022
DOI: 10.3390/JPM12091470
Abstract: Aggression risk assessments are vital to prevent injuries and morbidities amongst patients and staff in psychiatric settings. More recent studies have harnessed artificial intelligence (AI) methods such as machine learning algorithms to determine factors associated with aggression in psychiatric treatment settings. In this review, using Cooper’s five-stage review framework, we aimed to evaluate the: (1) predictive accuracy, and (2) clinical variables associated with AI-based aggression risk prediction amongst psychiatric inpatients. Databases including PubMed, Cochrane, Scopus, PsycINFO, CINAHL were searched for relevant articles until April 2022. The eight included studies were independently evaluated using critical appraisal tools for systematic review developed by Joanna Briggs Institute. Most of the studies (87.5%) examined health records in predicting aggression and reported acceptable to excellent accuracy with specific machine learning algorithms employed (area under curve range 0.75–0.87). No particular machine learning algorithm outperformed the others consistently across studies (area under curve range 0.61–0.87). Relevant factors identified with aggression related to demographic and social profile, past aggression, forensic history, other psychiatric history, psychopathology, challenging behaviors and management domains. The limited extant studies have highlighted a potential role for the use of AI methods to clarify factors associated with aggression in psychiatric inpatient treatment settings.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2019
End Date: 2024
Funder: Arts and Humanities Research Council
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