RESEARCH ARTICLE
Agreement on Web-based Diagnoses and Severity of Mental Health Problems in Norwegian Child and Adolescent Mental Health Services
Håkan Brøndbo 1, *, Børge Mathiassen 1, 2, Monica Martinussen 3, Einar Heiervang 4, Mads Eriksen 5, Siv Kvernmo 1, 2
Article Information
Identifiers and Pagination:
Year: 2012Volume: 8
First Page: 16
Last Page: 21
Publisher ID: CPEMH-8-16
DOI: 10.2174/1745017901208010016
Article History:
Received Date: 21/10/2011Revision Received Date: 24/1/2012
Acceptance Date: 28/1/2012
Electronic publication date: 22/3/2012
Collection year: 2012

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Abstract
Objective:
This study examined the agreement between diagnoses and severity ratings assigned by clinicians using a structured web-based interview within a child and adolescent mental health outpatient setting.
Method:
Information on 100 youths was obtained from multiple informants through a web-based Development and Well-Being Assessment (DAWBA). Based on this information, four experienced clinicians independently diagnosed (according to the International Classification of Diseases Revision 10) and rated the severity of mental health problems according to the Health of the Nation Outcome Scales for Children and Adolescents (HoNOSCA) and the Children’s Global Assessment Scale (C-GAS).
Results:
Agreement for diagnosis was κ=0.69-0.82. Intra-class correlation for single measures was 0.78 for HoNOSCA and 0.74 for C-GAS, and 0.93 and 0.92, respectively for average measures.
Conclusions:
Agreement was good to excellent for all diagnostic categories. Agreement for severity was moderate, but improved to substantial when the average of the ratings given by all clinicians was considered. Therefore, we conclude that experienced clinicians can assign reliable diagnoses and assess severity based on DAWBA data collected online.