scholarly journals Identifying Ethical Issues in Mental Health Research with Minors Adolescents: Results of a Delphi Study

2016 ◽  
Vol 13 (5) ◽  
pp. 489 ◽  
Author(s):  
Elisabeta Hiriscau ◽  
Nicola Stingelin-Giles ◽  
Danuta Wasserman ◽  
Stella Reiter-Theil
Keyword(s):  
Mental Health ◽  
Health Research ◽  
Delphi Study ◽  
Ethical Issues ◽  
Mental Health Research

scholarly journals Ethical issues in mental health research: the case for community engagement

2011 ◽  
Vol 24 (3) ◽  
pp. 208-214 ◽  
Author(s):  
James M DuBois ◽  
Brendolyn Bailey-Burch ◽  
Dan Bustillos ◽  
Jean Campbell ◽  
Linda Cottler ◽  
...  
Keyword(s):  
Mental Health ◽  
Community Engagement ◽  
Health Research ◽  
Ethical Issues ◽  
Mental Health Research

scholarly journals Ethical issues in perinatal mental health research

2009 ◽  
Vol 22 (6) ◽  
pp. 601-606 ◽  
Author(s):  
Anna R Brandon ◽  
Geetha Shivakumar ◽  
Simon Craddock Lee ◽  
Stephen J Inrig ◽  
John Z Sadler
Keyword(s):  
Mental Health ◽  
Health Research ◽  
Ethical Issues ◽  
Mental Health Research ◽  
Perinatal Mental Health

scholarly journals Co-development of a best practice checklist for mental health data science: A Delphi study

2021 ◽  
Author(s):  
E.J. Kirkham ◽  
C.J. Crompton ◽  
M.H. Iveson ◽  
I. Beange ◽  
A. McIntosh ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Health Research ◽  
Delphi Study ◽  
Best Practice ◽  
Data Science ◽  
Mental Health Research ◽  
Health Data ◽  
The Future ◽  
Three Phases

AbstractBackgroundMental health research is commonly affected by difficulties in recruiting and retaining participants, resulting in findings which are based on a sub-sample of those actually living with mental illness. Increasing the use of Big Data for mental health research, especially routinely-collected data, could improve this situation. However, steps to facilitate this must be enacted in collaboration with those who would provide the data - people with mental health conditions.MethodsWe used the Delphi method to create a best practice checklist for mental health data science. Twenty participants with both expertise in data science and personal experience of mental illness worked together over three phases. In the Phase 1, participants rated a list of 63 statements and added any statements or topics that were missing. Statements receiving a mean score of 5 or more (out of 7) were retained. These were then combined with the results of a rapid thematic analysis of participants’ comments to produce a 14-item draft checklist, with each item split into two components: best practice now and best practice in the future. In Phase 2, participants indicated whether or not each item should remain in the checklist, and items that scored more than 50% endorsement were retained. In Phase 3 participants rated their satisfaction with the final checklist.ResultsThe final checklist was made up of 14 “best practice” items, with each item covering best practice now and best practice in the future. At the end of the three phases, 85% of participants were (very) satisfied with the two best practice checklists, with no participants expressing dissatisfaction.ConclusionsIncreased stakeholder involvement is essential at every stage of mental health data science. The checklist produced through this work represents the views of people with experience of mental illness, and it is hoped that it will be used to facilitate trustworthy and innovative research which is inclusive of a wider range of individuals.

scholarly journals Co-development of a Best Practice Checklist for Mental Health Data Science: A Delphi Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Elizabeth J. Kirkham ◽  
Catherine J. Crompton ◽  
Matthew H. Iveson ◽  
Iona Beange ◽  
Andrew M. McIntosh ◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Health Research ◽  
Delphi Study ◽  
Best Practice ◽  
Data Science ◽  
Mental Health Research ◽  
Health Data ◽  
The Future ◽  
Three Phases

Background: Mental health research is commonly affected by difficulties in recruiting and retaining participants, resulting in findings which are based on a sub-sample of those actually living with mental illness. Increasing the use of Big Data for mental health research, especially routinely-collected data, could improve this situation. However, steps to facilitate this must be enacted in collaboration with those who would provide the data - people with mental health conditions.Methods: We used the Delphi method to create a best practice checklist for mental health data science. Twenty participants with both expertise in data science and personal experience of mental illness worked together over three phases. In Phase 1, participants rated a list of 63 statements and added any statements or topics that were missing. Statements receiving a mean score of 5 or more (out of 7) were retained. These were then combined with the results of a rapid thematic analysis of participants' comments to produce a 14-item draft checklist, with each item split into two components: best practice now and best practice in the future. In Phase 2, participants indicated whether or not each item should remain in the checklist, and items that scored more than 50% endorsement were retained. In Phase 3 participants rated their satisfaction with the final checklist.Results: The final checklist was made up of 14 “best practice” items, with each item covering best practice now and best practice in the future. At the end of the three phases, 85% of participants were (very) satisfied with the two best practice checklists, with no participants expressing dissatisfaction.Conclusions: Increased stakeholder involvement is essential at every stage of mental health data science. The checklist produced through this work represents the views of people with experience of mental illness, and it is hoped that it will be used to facilitate trustworthy and innovative research which is inclusive of a wider range of individuals.

Sign in / Sign up

Export Citation Format

Share Document