Web-based tools can be used reliably to detect patients with major depressive disorder and subsyndromal depressive symptoms

BACKGROUND: Although depression has been regarded as a major public health problem, many individuals with depression still remain undetected or untreated. Despite the potential for Internet-based tools to greatly improve the success rate of screening for depression, their reliability and validity has not been well studied. Therefore the aim of this study was to evaluate the test-retest reliability and criterion validity of a Web-based system, the Internet-based Self-assessment Program for Depression (ISP-D). METHODS: The ISP-D to screen for major depressive disorder (MDD), minor depressive disorder (MinD), and subsyndromal depressive symptoms (SSD) was developed in traditional Chinese. Volunteers, 18 years and older, were recruited via the Internet and then assessed twice on the online ISP-D system to investigate the test-retest reliability of the test. They were subsequently prompted to schedule face-to-face interviews. The interviews were performed by the research psychiatrists using the Mini-International Neuropsychiatric Interview and the diagnoses made according to DSM-IV diagnostic criteria were used for the statistics of criterion validity. Kappa (κ) values were calculated to assess test-retest reliability. RESULTS: A total of 579 volunteer subjects were administered the test. Most of the subjects were young (mean age: 26.2 ± 6.6 years), female (77.7%), single (81.6%), and well educated (61.9% college or higher). The distributions of MDD, MinD, SSD and no depression specified were 30.9%, 7.4%, 15.2%, and 46.5%, respectively. The mean time to complete the ISP-D was 8.89 ± 6.77 min. One hundred and eighty-four of the respondents completed the retest (response rate: 31.8%). Our analysis revealed that the 2-week test-retest reliability for ISP-D was excellent (weighted κ = 0.801). Fifty-five participants completed the face-to-face interview for the validity study. The sensitivity, specificity, positive, and negative predictive values for major depressive disorder were 81.8% and 72.7%, 66.7%, and 85.7% respectively. The overall accuracy was 76.4%. CONCLUSION: The evidence indicates the ISP-D is a reliable and valid online tool for assessing depression. Further studies should test the ISP-D in clinical settings to increase its applications in clinical environments with different populations and in a larger sample size

Mining association language patterns using a distributional semantic model for negative life event classification

AbstractPurposeNegative life events, such as the death of a family member, an argument with a spouse or the loss of a job, play an important role in triggering depressive episodes. Therefore, it is worthwhile to develop psychiatric services that can automatically identify such events. This study describes the use of association language patterns, i.e., meaningful combinations of words (e.g., <loss, job>), as features to classify sentences with negative life events into predefined categories (e.g., Family, Love, Work).MethodsThis study proposes a framework that combines a supervised data mining algorithm and an unsupervised distributional semantic model to discover association language patterns. The data mining algorithm, called association rule mining, was used to generate a set of seed patterns by incrementally associating frequently co-occurring words from a small corpus of sentences labeled with negative life events. The distributional semantic model was then used to discover more patterns similar to the seed patterns from a large, unlabeled web corpus.ResultsThe experimental results showed that association language patterns were significant features for negative life event classification. Additionally, the unsupervised distributional semantic model was not only able to improve the level of performance but also to reduce the reliance of the classification process on the availability of a large, labeled corpus

Evaluating the Effectiveness of a Telepresence-Enabled Cognitive Apprenticeship Model of Teacher Professional Development

This exploratory research used a mixed-methods design to compare the effectiveness of a telepresence-enabled cognitive apprenticeship model of teacher professional development (TEAM-PD) to that of a traditional workshop model by examining outcomes in teacher pedagogy and student achievement. Measures of the lll degree to which teachers in both groups enacted mathematics pedagogy provided mixed results. Both groups demonstrated similar patterns of behavior and cognition, indicating modest levels of pedagogy implementation. Although the experimental group demonstrated higher levels of enactment of the mathematics pedagogy, the comparison group demonstrated a faster rate of growth. Student outcome data were clear: students of teachers in the experimental group scored substantially higher on a test of relevant mathematics content than students of teachers in the comparison group. Collectively the results suggest that TEAM-PD has potential to be an effective model of teacher professional development

Behavioral Health Risk Assessment and Estimation: Validating an Integrated, Multi-Risk Factor Approach aided by Technology

High rates of behavioral health problems in the U.S. require integrated, multi-dimensional approaches. The study of behavioral health risk assessment and estimation aided by technology has the potential to inform assessment and management of behavioral health problems toward the goal of reducing adverse outcomes. The objective of this study is to inform evidence-based behavioral health risk assessment and estimation. This research examines the U.S. Army Medical Command data within the Behavioral Health Risk Management module (BHRM) to explore behavioral health risk assessment and estimation aided by technology. Analyses are conducted on BHRM data from the records of 30,263 U.S. Army active duty, Guard and Reserve service members assigned to military medical units (U.S. Army Warrior Transition Units) between September 1, 2009 and November 12, 2013. To test risk assessment, responses on the BHRM intake tool (Behavioral Health Risk Assessment-Questionnaire / BHRA-Q) are used to test prevalence, associations, internal reliability and questionnaire’s factor group structure. To examine risk estimation, statistical tests are completed on the prevalence and correlations of risk estimates by the BHRM and clinical providers as well as the predictive properties of demographic variables toward risk estimation. Hypotheses are supported for significant relationships among behavioral health risk variables (r = .40); good fit of the data to the eight-factor group structure of the BHRA-Q (Comparative Fit Index = 0.969; Tucker-Lewis Fit Index = 0.967; Root Mean Square Error of Approximation = .029 [90% Confidence Interval 0.029 - 0.030]); significant correlations among BHRM and provider risk estimates (large or medium effect size of BHRM on provider estimates); and three significant demographic predictors of risk estimation (race, religion and military service component). Internal reliability of BHRA-Q is supported (Cronbach’s α = .897). This study tests data related to an integrated, multi-risk factor behavioral health risk assessment questionnaire (BHRA-Q) and risk estimation aided by technology (BHRM). Findings support behavioral health risk assessment and estimation using evidence-based / informed multi-risk factor assessment, aided by technology, to inform clinical decision making. Although demographic variables are not strong predictors of risk estimation, as grouped and tested, further study is recommended.Social Work, Graduate College o