scholarly journals Analysis of the 2012 Canadian Community Health Survey-Mental Health demonstrates employment insecurity to be associated with mental illness

Medicine ◽  
2021 ◽  
Vol 100 (50) ◽  
pp. e28362
Author(s):  
Il-Ho Kim ◽  
Cyu-Chul Choi ◽  
Karen Urbanoski ◽  
Jungwee Park ◽  
Ji Man Kim
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Community Health ◽  
Health Survey ◽  
Canadian Community Health Survey ◽  
Employment Insecurity ◽  
Community Health Survey

497: Job Insecurity, Social Class and Inequalities in Mental Health using the Canadian Community Health Survey (CCHS) Cycle 1.2

2005 ◽  
Vol 161 (Supplement_1) ◽  
pp. S125-S125
Author(s):  
S A Ibrahim ◽  
C Muntaner ◽  
M Kerr ◽  
C Mustard ◽  
W Gnam
Keyword(s):  
Mental Health ◽  
Social Class ◽  
Community Health ◽  
Health Survey ◽  
Job Insecurity ◽  
Canadian Community Health Survey ◽  
Community Health Survey

Immigration as a social determinant of troubled sleep in Canada: some evidence from the Canadian Community Health Survey–Mental Health

Sleep Health ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 135-140 ◽  
Author(s):  
Yujiro Sano ◽  
Roger Antabe ◽  
Emmanuel Kyeremeh ◽  
Eugena Kwon ◽  
Jonathan Amoyaw
Keyword(s):  
Mental Health ◽  
Community Health ◽  
Health Survey ◽  
Canadian Community Health Survey ◽  
Social Determinant ◽  
Community Health Survey

scholarly journals The Canadian Community Health Survey: Mental Health and Well-Being

2005 ◽  
Vol 50 (10) ◽  
pp. 573-579 ◽  
Author(s):  
Ronald Gravel ◽  
Yves Béland
Keyword(s):  
Mental Health ◽  
Data Collection ◽  
Community Health ◽  
Health Survey ◽  
Well Being ◽  
Canadian Community Health Survey ◽  
Computer Assisted ◽  
Community Health Survey ◽  
Interviewer Training ◽  
Health And Well Being

As part of the Canadian Community Health Survey (CCHS) biennial strategy, the provincial survey component of the first CCHS cycle (Cycle 1.2) focused on different aspects of the mental health and well-being of Canadians living in private dwellings. Moreover, the survey collected data on prevalences of specific mental disorders and problems, use of mental health services, and economic and personal costs of having a mental illness. Data collection began in May 2002 and extended over 8 months. More than 85% of all interviews were conducted face-to-face and used a computer-assisted application. The survey obtained a national response rate of 77%. This paper describes several key aspects of the questionnaire content, the sample design, interviewer training, and data collection procedures. A brief overview of the CCHS regional component (Cycle 1.1) is also given.

scholarly journals Prediction of Suicidal Ideation in the Canadian Community Health Survey - Mental Health Component Using Deep Learning

2019 ◽  
Author(s):  
Sneha Desai ◽  
Myriam Tanguay-Sela ◽  
David Benrimoh ◽  
Robert Fratila ◽  
Eleanor Brown ◽  
...  
Keyword(s):  
Mental Health ◽  
Deep Learning ◽  
Suicidal Ideation ◽  
Community Health ◽  
Health Survey ◽  
Canadian Community Health Survey ◽  
Feature Model ◽  
Model Parameters ◽  
Community Health Survey ◽  
Patients At Risk

AbstractIntroductionSuicidal ideation (SI) is prevalent in the general population, and is a prominent risk factor for suicide. However, predicting which patients are likely to have SI remains a challenge. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete psychiatric datasets. An automated screening system for SI could help prompt clinicians to be more attentive to patients at risk for suicide.MethodsUsing the Canadian Community Health Survey - Mental Health Component, we trained a DL model based on 23,859 survey responses to predict lifetime SI on an individual patient basis. Models were created to predict both lifetime and last 12 month SI. We reduced 582 possible model parameters captured by the survey to 96 and 21 feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI respondents; validation was done on held-out data.ResultsAUC was used as the main model metric. For lifetime SI, the 96 feature model had an AUC of 0.79 and the 21 feature model had an AUC of 0.75. For SI in the last 12 months the 96 feature model had an AUC of 0.76 and the 21 feature model had an AUC of 0.69. DL outperformed random forest classifiers.DiscussionAlthough requiring further study to ensure clinical relevance and sample generalizability, this study is a proof-of-concept for the use of DL to improve prediction of SI. This kind of model would help start conversations with patients which could lead to improved care and, it is hoped, a reduction in suicidal behavior.

scholarly journals Identification of Suicidal Ideation in the Canadian Community Health Survey—Mental Health Component Using Deep Learning

2021 ◽  
Vol 4 ◽  
Author(s):  
Sneha Desai ◽  
Myriam Tanguay-Sela ◽  
David Benrimoh ◽  
Robert Fratila ◽  
Eleanor Brown ◽  
...  
Keyword(s):  
Mental Health ◽  
Deep Learning ◽  
Suicidal Ideation ◽  
Community Health ◽  
Health Survey ◽  
Canadian Community Health Survey ◽  
Feature Model ◽  
Sensitivity Analyses ◽  
Receiver Operating Curve ◽  
Community Health Survey

Introduction: Suicidal ideation (SI) is prevalent in the general population, and is a risk factor for suicide. Predicting which patients are likely to have SI remains challenging. Deep Learning (DL) may be a useful tool in this context, as it can be used to find patterns in complex, heterogeneous, and incomplete datasets. An automated screening system for SI could help prompt clinicians to be more attentive to patients at risk for suicide.Methods: Using the Canadian Community Health Survey—Mental Health Component, we trained a DL model based on 23,859 survey responses to classify patients with and without SI. Models were created to classify both lifetime SI and SI over the last 12 months. From 582 possible parameters we produced 96- and 21-feature versions of the models. Models were trained using an undersampling procedure that balanced the training set between SI and non-SI; validation was done on held-out data.Results: For lifetime SI, the 96 feature model had an Area under the receiver operating curve (AUC) of 0.79 and the 21 feature model had an AUC of 0.77. For SI in the last 12 months the 96 feature model had an AUC of 0.71 and the 21 feature model had an AUC of 0.68. In addition, sensitivity analyses demonstrated feature relationships in line with existing literature.Discussion: Although further study is required to ensure clinical relevance and sample generalizability, this study is an initial proof of concept for the use of DL to improve identification of SI. Sensitivity analyses can help improve the interpretability of DL models. This kind of model would help start conversations with patients which could lead to improved care and a reduction in suicidal behavior.

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