Reliability of the Dominic-R: A Young Child Mental Health Questionnaire Combining Visual and Auditory Stimuli

1997 ◽  
Vol 38 (6) ◽  
pp. 717-724 ◽  
Author(s):  
Jean-Pierre Valla ◽  
Lise Bergeron ◽  
Michelle Bidaut-Russell ◽  
Marie St-Georges ◽  
Nathalie Gaudet
Keyword(s):  
Mental Health ◽  
Young Child ◽  
Child Mental Health ◽  
Auditory Stimuli ◽  
Health Questionnaire ◽  
Mental Health Questionnaire

Attitudes About Child Mental Health Questionnaire

2014 ◽  
Author(s):  
Craig Anne Heflinger ◽  
Kenneth A. Wallston ◽  
Abraham Mukolo ◽  
Ana Maria Brannan
Keyword(s):  
Mental Health ◽  
Child Mental Health ◽  
Health Questionnaire ◽  
Mental Health Questionnaire

scholarly journals A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jakub Tomasik ◽  
Sung Yeon Sarah Han ◽  
Giles Barton-Owen ◽  
Dan-Mircea Mirea ◽  
Nayra A. Martin-Key ◽  
...  
Keyword(s):  
Mental Health ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Diagnostic Algorithm ◽  
Health Questionnaire ◽  
Major Depressive ◽  
Blood Biomarker ◽  
Mental Health Questionnaire ◽  
Biomarker Data

AbstractThe vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

Worries About Mental Health Questionnaire

2009 ◽  
Author(s):  
Michael Bassett ◽  
David Sperlinger ◽  
Daniel Freeman
Keyword(s):  
Mental Health ◽  
Health Questionnaire ◽  
Mental Health Questionnaire

Reliability and validity of the Positive Mental Health Questionnaire in a sample of Spanish university students

2017 ◽  
Vol 24 (2-3) ◽  
pp. 123-133 ◽  
Author(s):  
J. Roldán-Merino ◽  
M. T. Lluch-Canut ◽  
I. Casas ◽  
M. Sanromà-Ortíz ◽  
C. Ferré-Grau ◽  
...  
Keyword(s):  
Mental Health ◽  
University Students ◽  
Reliability And Validity ◽  
Positive Mental Health ◽  
Health Questionnaire ◽  
Mental Health Questionnaire
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