scholarly journals Pooling Mental Health Data with Chatbots

2021 ◽  
pp. 70-97
Author(s):  
Michael Mattioli
Keyword(s):  
Mental Health ◽  
Health Data

scholarly journals Police Encounters as Stressors: Associations with Depression and Anxiety across Race

2021 ◽  
Vol 7 ◽  
pp. 237802312199812
Author(s):  
Sirry Alang ◽  
Donna McAlpine ◽  
Malcolm McClain
Keyword(s):  
Mental Health ◽  
Depressed Mood ◽  
Social Stress ◽  
The United States ◽  
Health Data ◽  
Police Brutality ◽  
Urban Residents ◽  
Depression And Anxiety ◽  
Police Encounters ◽  
The Relationship

Stress researchers have emphasized the relationship between social stress and mental health. However, research investigating police brutality as a stressor is scarce. The authors conceptualize police brutality as a stressor, examining racial variation in its effects on mental health. Data came from the Survey of the Health of Urban Residents in the United States ( n = 4,389). Negative encounters with the police were found to be associated with depressed mood and anxiety. The relationship between encounters with the police and depressed mood was stronger among Black respondents and Latinxs compared with Whites. Regardless of personal encounters with the police, the anticipatory stress of police brutality—concern that one might become a victim of police brutality—was associated with depression and anxiety. These findings highlight police brutality as an anticipatory stressor and have implications for whiteness as a resource that protects from the stress of negative police encounters.

scholarly journals Using Bayesian Model Averaging to Analyse Hierarchical Health Data: model implementation and application to linked health service use data

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Behrooz Hassani-Mahmooei ◽  
Janneke Berecki-Gisolf ◽  
Alex Collie
Keyword(s):  
Mental Health ◽  
Health Service ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Medical Costs ◽  
Health Data ◽  
Post Injury ◽  
The Cost

ABSTRACTObjectiveThe majority of standard coding systems applied to health data are hierarchical: they start with several major categories and then each category is broken into subcategories across multiple levels. Running statistical models on these datasets, may lead to serious methodological challenges such as multicollinearity between levels or selecting suboptimal models as model space grows exponentially by adding each new level. The aim of this presentation is to introduce an analytical framework that addresses this challenge. ApproachData was from individuals who claimed Transport Accident Commission (TAC) compensation for motor vehicle accidents that occurred between 2010 and 2012 in the state of Victoria, Australia and provided consent for Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Schedule (MBS) linkage (n=738). PBS and MBS records dating from 12 months prior to injury were provided by the Department of Human Services (Canberra, Australia). Pre-injury use of health service items and pharmaceuticals were considered to indicate pre-existing health conditions. Both MBS and PBS listings have a hierarchical structure. The outcome was the cost of recovery; this was also hierarchical across four level (e.g. total, medical, consultations, and specialist). A Bayesian Model Averaging model was embedded into a data mining framework which automatically created all the cost outcomes and selected the best model after penalizing for multicollinearity. The model was run across multiple prior settings to ensure robustness. Monash University’s High Performance Computing Cluster was used for running approximately 5000 final models.ResultsThe framework successfully identified variables at different levels of hierarchy as indicators of pre-existing conditions that affect cost of recovery. For example, according to the results, on average, patients who received prescription pain or mental health related medication before the injury had 31.2% higher short-term and 36.9% higher long-term total recovery cost. For every anaesthetic in the year before the accident, post-injury hospital cost increased by 24%, for patients with anxiety it increased by 35.4%. For post-injury medical costs, every prescription of drugs used in diabetes (Category A10 in ATC) increased the cost by 8%, long term medical costs were affected by both pain and mental health. ConclusionBayesian model averaging provides a robust framework for mining hierarchically linked health data helping researchers to identify potential associations which may not have been discovered using conventional technique and also preventing them from identifying associations that are sporadic but not robust.

scholarly journals Mental health professionals’ perceptions on patients control of data sharing

2020 ◽  
Vol 26 (3) ◽  
pp. 2011-2029 ◽  
Author(s):  
Julia Ivanova ◽  
Adela Grando ◽  
Anita Murcko ◽  
Michael Saks ◽  
Mary Jo Whitfield ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Information ◽  
Data Sharing ◽  
Mental Health Professionals ◽  
Health Professionals ◽  
Health Care Professionals ◽  
Health Data ◽  
Structured Interviews ◽  
Timely Access ◽  
The Impact

Integrated mental and physical care environments require data sharing, but little is known about health professionals’ perceptions of patient-controlled health data sharing. We describe mental health professionals’ views on patient-controlled data sharing using semi-structured interviews and a mixed-method analysis with thematic coding. Health information rights, specifically those of patients and health care professionals, emerged as a key theme. Behavioral health professionals identified patient motivations for non-sharing sensitive mental health records relating to substance use, emergency treatment, and serious mental illness (94%). We explore conflicts between professional need for timely access to health information and patient desire to withhold some data categories. Health professionals’ views on data sharing are integral to the redesign of health data sharing and informed consent. As well, they seek clarity about the impact of patient-controlled sharing on health professionals’ roles and scope of practice.

scholarly journals Mysticism and mental health: A critical dialogue

2010 ◽  
Vol 66 (2) ◽  
Author(s):  
George Drazenovich ◽  
Celia Kourie
Keyword(s):  
Mental Health ◽  
General Population ◽  
Mystical Experience ◽  
Historical Context ◽  
Health Data ◽  
Psychotic Experience ◽  
Critical Dialogue ◽  
Clinical Criteria ◽  
Mystical Tradition ◽  
Opening Up

Contemporary research suggests that a path is now open for critical dialogue between mysticism and mental health. Data are accumulating regarding the frequency with which mystical experience occurs in the general population. Social science researchers are undertaking studies to determine whether people can knowledgably differentiate between the presence of a mystical experience and other types of experience that occur in their lives. Psychologists are developing clinical criteria by which the mystical and psychotic experience can be differentiated. Neuropsychiatric researchers are exploring the effect of the mystical experience by way of enhanced brain imagery. Theologians are opening up the received wisdom of the mystical tradition and applying it to the present historical context. This paper drew these diverse disciplines together to demonstrate an emerging consensus with respect to the efficacy of mysticism in the field of mental health.

scholarly journals P45 The acceptability of using genetic information to guide treatment of asthma to children, young people, and parents (pilot study)

2019 ◽  
Vol 104 (6) ◽  
pp. e35.3-e36
Author(s):  
C King ◽  
L Bracken ◽  
E McDonough ◽  
M Pirmohamed ◽  
M Peak ◽  
...  
Keyword(s):  
Mental Health ◽  
Young People ◽  
Childhood Asthma ◽  
Genetic Information ◽  
Data Privacy ◽  
Personalised Medicine ◽  
Genomic Data ◽  
Health Data ◽  
Special Focus ◽  
Legal Guardians

BackgroundThere are multiple pharmacogenomic studies in children’s asthma. It has not been established how (or if) children, young people or their parents/legal guardians would accept use of their genetic information to guide their treatment.AimTo determine the views of CYP, and parents/legal guardians, on aspects of using genetic testing to guide management of childhood asthma.MethodsFocus group session with both the Liverpool’s young people advisory group (YPAG), and Parents’ group, at Alder Hey Children’s Hospital. Group members completed anonymous questionnaires determining the importance and privacy associated with different themes of data, with a special focus on health data.ResultsThere were 11 responders, five parents/guardians and six CYP. Both the parents and the CYP considered personal data, such as date of birth, NI number and name, both the most important and the most private. Health data was considered the second most important, and private, although parents rated data from social media data an equal second in terms of privacy. Within healthcare data, CYP considered data regarding their mental health, followed by medical conditions and genomic data, as the sources to be of highest importance. Parents considered their child’s illnesses most important, followed by genomic data. In relation to privacy, CYP considered genomic data first followed by information concerning their mental health. The parents considered genomic data highest for data privacy.ConclusionFrom this session it is clear that health data in general, and genetic data in particular, has a high value of importance to CYP and parents, but there are variations in how data is prioritised. These pilot data will inform a large scale patient and parent acceptability study in personalised medicine and childhood asthma (CHANGE study).Disclosure(s)Nothing to disclose

scholarly journals The Mental Health of Adults with Developmental Disabilities in Ontario: Lessons from Administrative Health Data

2018 ◽  
Vol 21 (1) ◽  
pp. 6-9 ◽  
Author(s):  
Yona Lunsky ◽  
Robert Balogh ◽  
Anna Durbin ◽  
Avra Selick ◽  
Tiziana Volpe ◽  
...  
Keyword(s):  
Mental Health ◽  
Developmental Disabilities ◽  
Health Data ◽  
Administrative Health Data ◽  
Adults With Developmental Disabilities

The S-Index and the National Institute of Mental Health Data Archive

2017 ◽  
Vol 74 (5) ◽  
pp. 540
Author(s):  
Svetlana Novikova ◽  
Dan Hall ◽  
Gregory K. Farber
Keyword(s):  
Mental Health ◽  
Health Data ◽  
Data Archive
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