scholarly journals Machine Learning in Clinical Psychology and Psychotherapy Education: A Survey of Postgraduate Students at a Swiss University

2020 ◽  
Author(s):  
Charlotte Blease ◽  
Anna Kharko ◽  
Marco Annoni ◽  
Jens Gaab ◽  
Cosima Locher
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
Mental Health Care ◽  
Clinical Psychology ◽  
Big Data Analytics ◽  
Mental Healthcare ◽  
Convenience Sample ◽  
Postgraduate Students ◽  
Psychotherapy Education

AbstractBackgroundThere is increasing use of for machine learning-enabled tools (e.g., psychotherapy apps) in mental health care.ObjectiveThis study aimed to explore postgraduate clinical psychology and psychotherapy students’ familiarity and formal exposure to topics related to artificial intelligence and machine learning (AI/ML) during their studies.MethodsIn April-June 2020, we conducted a mixed-methods web-based survey using a convenience sample of 120 clinical psychology and psychotherapy enrolled in a two-year Masters’ program students at a Swiss university.ResultsIn total 37 students responded (response rate: 37/120, 31%). Among the respondents, 73% (n=27) intended to enter a mental health profession. Among the students 97% reported that they had heard of the term ‘machine learning,’ and 78% reported that they were familiar with the concept of ‘big data analytics’. Students estimated 18.61/3600 hours, or 0.52% of their program would be spent on AI/ML education. Around half (46%) reported that they intended to learn about AI/ML as it pertained to mental health care. On 5-point Likert scale, students moderately agreed (median=4) that AI/M should be part of clinical psychology/psychotherapy education.ConclusionsEducation programs in clinical psychology/psychotherapy may lag developments in AI/ML-enabled tools in mental healthcare. This survey of postgraduate clinical psychology and psychotherapy students raises questions about how curricula could be enhanced to better prepare clinical psychology/psychotherapy trainees to engage in constructive debate about ethical and evidence-based issues pertaining to AI/ML tools, and in guiding patients on the use of online mental health services and apps.

scholarly journals Machine Learning in Clinical Psychology and Psychotherapy Education: A Mixed Methods Pilot Survey of Postgraduate Students at a Swiss University

2021 ◽  
Vol 9 ◽  
Author(s):  
Charlotte Blease ◽  
Anna Kharko ◽  
Marco Annoni ◽  
Jens Gaab ◽  
Cosima Locher
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
Mixed Methods ◽  
Pilot Study ◽  
Clinical Psychology ◽  
Mental Healthcare ◽  
Psychology Students ◽  
Clinical Psychology Students ◽  
Psychotherapy Education

Background: There is increasing use of psychotherapy apps in mental health care.Objective: This mixed methods pilot study aimed to explore postgraduate clinical psychology students' familiarity and formal exposure to topics related to artificial intelligence and machine learning (AI/ML) during their studies.Methods: In April-June 2020, we conducted a mixed-methods online survey using a convenience sample of 120 clinical psychology students enrolled in a two-year Masters' program at a Swiss University.Results: In total 37 students responded (response rate: 37/120, 31%). Among respondents, 73% (n = 27) intended to enter a mental health profession, and 97% reported that they had heard of the term “machine learning.” Students estimated 0.52% of their program would be spent on AI/ML education. Around half (46%) reported that they intended to learn about AI/ML as it pertained to mental health care. On 5-point Likert scale, students “moderately agreed” (median = 4) that AI/M should be part of clinical psychology/psychotherapy education. Qualitative analysis of students' comments resulted in four major themes on the impact of AI/ML on mental healthcare: (1) Changes in the quality and understanding of psychotherapy care; (2) Impact on patient-therapist interactions; (3) Impact on the psychotherapy profession; (4) Data management and ethical issues.Conclusions: This pilot study found that postgraduate clinical psychology students held a wide range of opinions but had limited formal education on how AI/ML-enabled tools might impact psychotherapy. The survey raises questions about how curricula could be enhanced to educate clinical psychology/psychotherapy trainees about the scope of AI/ML in mental healthcare.

scholarly journals The Quality Mental Health Care Network: A roadmap to improving quality mental healthcare in Canada

2021 ◽  
Vol 34 (2) ◽  
pp. 100-106
Author(s):  
Emily J. Follwell ◽  
Siri Chunduri ◽  
Claire Samuelson-Kiraly ◽  
Nicholas Watters ◽  
Jonathan I. Mitchell
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Focus Groups ◽  
Lived Experiences ◽  
Mental Healthcare ◽  
Care Network ◽  
Key Informant ◽  
Health Care Network

Although there are numerous quality of care frameworks, little attention has been given to the essential concepts that encompass quality mental healthcare. HealthCare CAN and the Mental Health Commission of Canada co-lead the Quality Mental Health Care Network (QMHCN), which has developed a quality mental healthcare framework, building on existing provincial, national, and international frameworks. HealthCare CAN conducted an environmental scan, key informant interviews, and focus groups with individuals with lived experiences to develop the framework. This article outlines the findings from this scan, interviews and focus groups.

scholarly journals Alternative medicine under the Mental Health Care Act, 2017: Future implications and concerns

2022 ◽  
Vol 07 (01) ◽  
pp. 37-41
Author(s):  
Ramdas Ransing ◽  
Sujita Kumar Kar ◽  
Vikas Menon
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Alternative Medicine ◽  
Mental Healthcare ◽  
Evidence Base ◽  
Evidence Based ◽  
Insufficient Evidence ◽  
Indian Government ◽  
Evidence Based Care

In recent years, the Indian government has been promoting healthcare with an insufficient evidence base, or which is non-evidence-based, alongside delivery of evidence-based care by untrained practitioners, through supportive legislation and guidelines. The Mental Health Care Act, 2017, is a unique example of a law endorsing such practices. In this paper, we aim to highlight the positive and negative implications of such practices for the delivery of good quality mental healthcare in India.

scholarly journals Identifying Military Veterans in a Clinical Research Database using Natural Language Processing

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Daniel Leightley ◽  
Katharine M Mark ◽  
David Pernet ◽  
Dominic Murphy ◽  
Nicola T Fear ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Care ◽  
National Health Service ◽  
Mental Health Care ◽  
Health Care Services ◽  
Automatic Machine ◽  
Machine Learning Method ◽  
Learning Method ◽  
Care Services

BackgroundThere is a lack of quantitative evidence concerning United Kingdom veterans who access secondary mental health care. This is mainly due to a person’s veteran status not being routinely collected when they enter the health care system. Main AimThe study aimed to develop an NLP approach for identifying veterans accessing secondary mental health care services using National Health Service electronic health records. MethodsVeterans were identified using the South London and Maudsley Biomedical Research Centre (SLaM) case register – a database holding secondary mental health care electronic records for the South London and Maudsley National Health Service Trust of 300,000 patients. We developed two methods. An NLP and machine learning tool were developed to automatically evaluate personal history statements written by clinicians. ResultsThis study showed that it was possible to identify veterans using the NLP and machine learning approach on a sub-set of 4,200 patients. The automatic machine learning method was able to identify 270 veterans representing an accuracy of 97.2%. It is estimated to take between 6 to 16 minutes to manually search patient history statements whereas the automatic machine learning method took only one minute to run. ConclusionWe have shown that it is possible to identify veterans using NLP combined with machine learning. This work contributes towards the development of a more comprehensive picture of veterans who are accessing secondary mental health care services in the UK. It represents a first step in identifying veterans from one dataset and we hope that future research can inform the possibility of deploying the methods nationally. Despite our success in the current work, the tools are tailored to the SLaM dataset and future work is needed to develop a more agnostic framework. FundingForces in Mind Trust

scholarly journals Efforts and Challenges to Ensure Continuity of Mental Healthcare Service Delivery in a Low Resource Settings During COVID-19 Pandemic—A Case of a Kenyan Referral Hospital

2021 ◽  
Vol 11 ◽  
Author(s):  
Edith Kwobah ◽  
Florence Jaguga ◽  
Kiptoo Robert ◽  
Elias Ndolo ◽  
Jane Kariuki
Keyword(s):  
Mental Health ◽  
Primary Care ◽  
Health Care ◽  
Service Delivery ◽  
Mental Health Care ◽  
Healthcare Service ◽  
Healthcare Delivery ◽  
Mental Healthcare ◽  
Referral Hospital ◽  
Number Of Patients

The rising number of patients with Covid-19 as well as the infection control measures have affected healthcare service delivery, including mental healthcare. Mental healthcare delivery in low and middle income countries where resources were already limited are likely to be affected more during this pandemic. This paper describes the efforts of ensuring mental healthcare delivery is continued in a referral hospital in Kenya, Moi Teaching and Referral hospital, as well as the challenges faced. These efforts are guided by the interim guidelines developed by the Kenyan ministry of health. Some of the adjustments described includes reducing number of patients admitted, shortening the stay in the inpatient setting, using outdoors for therapy to promote physical distancing, utilization of electronic platforms for family therapy sessions, strengthening outpatient services, and supporting primary care workers to deliver mental health care services. Some of the challenges include limited ability to move about, declining ability for patients to pay out of pocket due to the economic challenges brought about by measures to control Covid-19, limited drug supplies in primary care facilities, inability to fully implement telehealth due to connectivity issues and stigma for mental health which results in poor social support for the mentally ill patients. It is clear that current pandemic has jeopardized the continuity of usual mental healthcare in many settings. This has brought to sharp focus the need to decentralize mental health care and promote community based services. Meanwhile, there is need to explore feasible alternatives to ensure continuity of care.

scholarly journals Working on and with Relationships: Relational Work and Spatial Understandings of Good Care in Community Mental Healthcare in Trieste

2020 ◽  
Vol 44 (4) ◽  
pp. 544-564
Author(s):  
Christien Muusse ◽  
Hans Kroon ◽  
Cornelis L. Mulder ◽  
Jeannette Pols
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Community Mental Health ◽  
Mental Health Care ◽  
Mental Healthcare ◽  
Care Practice ◽  
Community Mental Health Care ◽  
Good Care ◽  
Daily Care ◽  
Spatial Metaphors

Abstract Deinstitutionalization is often described as an organizational shift of moving care from the psychiatric hospital towards the community. This paper analyses deinstitutionalization as a daily care practice by adopting an empirical ethics approach instead. Deinstitutionalization of mental healthcare is seen as an important way of improving the quality of lives of people suffering from severe mental illness. But how is this done in practice and which different goods are strived for by those involved? We examine these questions by giving an ethnographic description of community mental health care in Trieste, a city that underwent a radical process of deinstitutionalization in the 1970s. We show that paying attention to the spatial metaphors used in daily care direct us to different notions of good care in which relationships are central. Addressing the question of how daily care practices of mental healthcare outside the hospital may be constituted and the importance of spatial metaphors used may inform other practices that want to shape community mental health care.

scholarly journals Clinical Psychology during COVID-19: Experiences from Six Frontline Hospitals in Mexico

2021 ◽  
Vol 32 (120) ◽  
pp. 143-155
Author(s):  
Edgar Landa-Ramírez ◽  
Cintia Tamara Sánchez-Cervantes ◽  
Sofía Sánchez-Román ◽  
Eryka del Carmen Urdapilleta-Herrera ◽  
Jorge Luis Basulto-Montero ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Clinical Psychology ◽  
Health Personnel ◽  
Health Workers ◽  
Hospital Personnel ◽  
Local Hospital ◽  
The World ◽  
Hospital Systems

Around the world, the COVID-19 pandemic has generated clinical challenges for health personnel in general and hospital personnel in particular. In Mexico, the clinical psychologists who are part of the local hospital systems have adapted professional practices to provide mental health care in COVID-19 frontline hospitals. This text describes the actions and challenges arising from treating patients, families, and health workers in six COVID-19 hospitals in Mexico. It highlights the main problems identified, strategies to address them, and the barriers encountered during this pandemic. Finally, this document may be useful for planning clinical psychological activities within COVID-19 hospitals in places where new waves of contagion appear.

scholarly journals Integrating eHealth within a Transforming Mental Healthcare Setting: A Qualitative Study into Values, Challenges, and Prerequisites

2021 ◽  
Vol 18 (19) ◽  
pp. 10287
Author(s):  
Karin Lorenz-Artz ◽  
Joyce Bierbooms ◽  
Inge Bongers
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Mental Healthcare ◽  
Healthcare Setting ◽  
High Impact ◽  
Future Research ◽  
Community Based ◽  
Main Challenge ◽  
Care Models

Mental health care is shifting towards more person-centered and community-based health care. Although integrating eHealth within a transforming healthcare setting may help accomplishing the shift, research studying this is lacking. This study aims to improve our understanding of the value of eHealth within a transforming mental healthcare setting and to define the challenges and prerequisites for implementing eHealth in particular within this transforming context. In this article, we present the results of 29 interviews with clients, social network members, and professionals of an ambulatory team in transition within a Dutch mental health care institute. The main finding is that eHealth can support a transforming practice shifting towards more recovery-oriented, person-centered, and community-based service in which shared-decision making is self-evident. The main challenge revealed is how to deal with clients’ voices, when professionals see the value of eHealth but clients do not want to start using eHealth. The shift towards client-centered and network-oriented care models and towards blended care models are both high-impact changes in themselves. Acknowledging the complexity of combining these high-impact changes might be the first step towards creating blended client-centered and network-oriented care. Future research should examine whether and how these substantial shifts could be mutually supportive.

The History of Psychological Psychotherapy in Germany: The Rise of Psychology in Mental Health Care and the Emergence of Clinical Psychology During the 20th Century

2020 ◽  
Author(s):  
Lisa Malich
Keyword(s):  
Mental Health ◽  
Health Care ◽  
World War Ii ◽  
Mental Health Care ◽  
20Th Century ◽  
Clinical Psychology ◽  
West Germany ◽  
History Of ◽  
1960S And 1970S ◽  
Professional Fields

Two different but related developments played an important role in the history of psychologists in the fields of mental health care in Germany during the 20th century. The first development took place in the field of applied psychology, which saw psychological professionals perform mental testing, engage in counseling and increasingly, in psychotherapy in practical contexts. This process slowly began in the first decades of the 20th century and included approaches from different schools of psychotherapy. The second relevant development was the emergence of clinical psychology as an academic sub-discipline of psychology. Having become institutionalized in psychology departments at German universities during the 1960s and 1970s, clinical psychology often defines itself as a natural science and almost exclusively focuses on cognitive-behavioral approaches. There are four phases of the growing relationship between psychology and psychotherapy in Germany in which the two developments were increasingly linked: first, the entry of psychology into psychiatric and psychotherapeutic fields from approximately 1900 until 1945; second, the rise of psychological psychotherapy and the emergence of clinical psychology after World War II until 1972, when the diploma-regulations in West Germany were revised; third, a phase of consolidation and diversification from 1973 until the pivotal psychotherapy law of 1999; and fourth, the shifting equilibrium as established profession and discipline up to the reform of the psychotherapy law in 2019. Overall, the emergence of psychological psychotherapy has not one single trajectory but rather multiple origins in the different and competing academic and professional fields of mental health care.

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