Predicting type of psychiatric disorder from Strengths and Difficulties Questionnaire (SDQ) scores in child mental health clinics in London and Dhaka

2000 ◽  
Vol 9 (2) ◽  
pp. 129-134 ◽  
Author(s):  
R. Goodman ◽  
D. Renfrew ◽  
M. Mullick
2012 ◽  
Vol 200 (5) ◽  
pp. 426-427 ◽  
Author(s):  
Anna Goodman ◽  
Robert Goodman

SummaryBritish local authorities are required to monitor the mental health of looked after children using mean Strengths and Difficulties Questionnaire (SDQ) scores from parents or carers. This assumes that differences in mean SDQ scores reflect genuine differences in child mental health in this group, something we examined using nationally representative surveys (n = 1391, age 5–16). We found that the SDQ was a genuinely dimensional measure of mental health in these children and provided accurate estimates of disorder prevalence.


2012 ◽  
Vol 60 (5) ◽  
pp. S159
Author(s):  
G.J. Wergeland ◽  
K. Fjermestad ◽  
C. Marin ◽  
W. Silverman ◽  
L.G. Öst ◽  
...  

2014 ◽  
Vol 65 (12) ◽  
pp. 1439-1444 ◽  
Author(s):  
Ka Ho Brian Chor ◽  
Su-Chin Serene Olin ◽  
Jamie Weaver ◽  
Andrew F. Cleek ◽  
Mary M. McKay ◽  
...  

1998 ◽  
Vol 3 (2) ◽  
pp. 68-71 ◽  
Author(s):  
Malcolm Wiseman ◽  
Marie McBride

Non-attendance for first outpatient appointments at Child Mental Health Clinics is a common problem affecting efficient use of resources, as well as staff morale. This study demonstrates that requiring families to confirm that they want an appointment, in order to receive one, significantly improves the attendance rate for first appointments, compared to two other commonly used procedures: a reminder letter before the appointment is sent, and a standard appointment letter. Furthermore, this procedure did not appear to reduce accessibility to the service.


2020 ◽  
Vol 30 (3) ◽  
pp. 423-439
Author(s):  
Christin Mujica ◽  
Kiara Alvarez ◽  
Shalini Tendulkar ◽  
Mario Cruz-Gonzalez ◽  
Margarita Alegría

2020 ◽  
Author(s):  
Lydia Gabriela Speyer ◽  
Anastasia Ushakova ◽  
Hildigunnur Anna Hall ◽  
Michelle Luciano ◽  
Bonnie Auyeung ◽  
...  

Background: Most mental health difficulties have their onset during childhood and adolescence. Many children who suffer from one mental health issue also suffer from at least one comorbid disorder. Autoregressive latent trajectory models with structured residuals (ALT-SR) and multilevel graphical vector autoregression (GVAR) are recent complementary approaches that can help provide new insights into the reciprocal relationships between multiple mental health domains and advance the understanding of the development of comorbidities.Methods: This study uses ALT-SR and multilevel GVAR models to analyse the temporal, contemporaneous and between-person relationships between emotional problems, peer problems, conduct problems, hyperactivity/inattention and prosociality as measured by the parent-reported Strengths and Difficulties Questionnaire (SDQ) in 17,478 children taking part in the UK Millennium Cohort Study at ages 3, 5, 7, 11, 14 and 17 years. Results: Results from both the ALT-SR model and the multilevel GVAR model highlight that children’s strengths and difficulties in different domains of psychosocial functioning dynamically influence each other over- and within-time. The ALT-SR model highlighted that hyperactivity/inattention plays a central role in affecting other domains over developmental time while the GVAR model highlighted comparably strong bidirectional relationships between conduct problems and prosociality as well as between emotional problems and peer problems. Both models suggest that most domains are also related to each other over shorter timescales.Conclusion: This study highlights that mental health difficulties influence one another dynamically over time. As illustrated in the domains of the SDQ, these dynamic changes can be modelled using the complementary techniques of ALT-SR or GVAR models, each offering different insights into the nature of comorbidity.


Author(s):  
Johanna B. Folk ◽  
Marissa A. Schiel ◽  
Rachel Oblath ◽  
Vera Feuer ◽  
Aditi Sharma ◽  
...  

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