TY - JOUR A1 - Adnan, Hassan Sami A1 - Srsic, Amanda A1 - Venticich, Pete Milos A1 - Townend, David M.R. T1 - Using AI for mental health analysis and prediction in school surveys JF - European journal of public health N2 - Background: Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models. Methods: For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases. Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning can then be deployed to classify specific behaviour and mental health patterns. Results: We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project. Conclusions: This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns of unknown effects. KW - ethics KW - artificial intelligence KW - adolescent KW - child KW - confidentiality KW - health personnel KW - mental disorders KW - mental health KW - personal satisfaction KW - privacy KW - school (environment) KW - statutes and laws KW - public health medicine KW - surveillance KW - medical KW - prevention KW - datasets KW - machine learning KW - supervised machine learning KW - data analysis Y1 - 2020 U6 - https://doi.org/10.1093/eurpub/ckaa165.336 SN - 1101-1262 SN - 1464-360X VL - 30 SP - V125 EP - V125 PB - Oxford Univ. Press CY - Oxford [u.a.] ER - TY - JOUR A1 - Muschalla, Beate A1 - Henning, Anne A1 - Haake, Tim Woody A1 - Cornetz, Kathrin A1 - Olbrich, Dieter T1 - Mental health problem or workplace problem or something else BT - what contributes to work perception? JF - Disability and rehabilitation : an international, multidisciplinary journal N2 - Purpose: Work perception is an important predictor for work ability and, therefore, of interest for rehabilitation. Until now it is unclear to which extent different psychological aspects explain work perception. This study investigates in which way workplace problems on the one hand, and mental health and coping on the other hand, contribute to work perception. Methods: A heterogeneous sample of 384 persons in working age with and without mental health problems was recruited. Participants gave self-reports on workplace problems, mental health problems, work-coping, work-anxiety, and work perception. Results: Persons with mental health problems and workplace problems (M + W) perceive the highest degree of work demands, followed by persons with workplace problems but without mental health problems (NM + W). Work-anxiety appeared as the strongest factor explaining perception of high work demands, whereas general mental health problems did not contribute significantly to variance explanation. Conclusions: Persons with specific mental health problems in terms of work-anxiety may be expected to perceive higher work demands. They may be detected when asking for work perception, e.g., within the frame of return-to-work interventions in rehabilitation, or in occupational health settings by mental hazard analysis. KW - work ability KW - work anxiety KW - workplace KW - mental disorders KW - rehabilitation KW - work perception Y1 - 2018 U6 - https://doi.org/10.1080/09638288.2018.1501099 SN - 0963-8288 SN - 1464-5165 VL - 42 IS - 4 SP - 502 EP - 509 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - JOUR A1 - Ay-Bryson, Destina Sevde A1 - Weck, Florian A1 - Kühne, Franziska T1 - Can simulated patient encounters appear authentic? BT - development and pilot results of a rating instrument based on the portrayal of depressive patients JF - Training and education in professional psychology N2 - Public Significance Statement This study demonstrates that simulated patients (SPs) can authentically portray a depressive case. The results provide preliminary evidence of psychometrically sound properties of the rating scale that contributes to distinguishing between authentic and unauthentic SPs and may thus foster SPs' dissemination into evidence-based training.
For training purposes, simulated patients (SPs), that is, healthy people portraying a disorder, are disseminating more into clinical psychology and psychotherapy. In the current study, we developed an observer-based rating instrument for the evaluation of SP authenticity-namely, it not being possible to distinguish them from real patients-so as to foster their use in evidence-based training. We applied a multistep inductive approach to develop the Authenticity of Patient Demonstrations (APD) scale. Ninety-seven independent psychotherapy trainees, 77.32% female, mean age of 31.49 (SD = 5.17) years, evaluated the authenticity of 2 independent SPs, each of whom portrayed a depressive patient. The APD demonstrated good internal consistency (Cronbach's alpha = .83) and a strong correlation (r = .82) with an established tool for assessing SP performance in medical contexts. The APD scale distinguished significantly between an authentic and unauthentic SP (d = 2.35). Preliminary evidence for the psychometric properties of the APD indicates that the APD could be a viable tool for recruiting, training, and evaluating the authenticity of SPs. Strengths, limitations, and future directions are also discussed in detail. KW - authenticity KW - evidence-based training KW - standardized patients KW - role-play KW - mental disorders Y1 - 2022 U6 - https://doi.org/10.1037/tep0000349 SN - 1931-3918 SN - 1931-3926 VL - 16 IS - 1 SP - 20 EP - 27 PB - American Psychological Association CY - Washington ER -