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Record W2117112642 · doi:10.2196/jmir.5.3.e23

A Web-Based Screening Instrument for Depression and Anxiety Disorders in Primary Care

2003· article· en· W2117112642 on OpenAlexaffabout
Peter Farvolden, Carolina McBride, R. Michael Bagby, Paula Ravitz

Bibliographic record

VenueJournal of Medical Internet Research · 2003
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsAnxietyPrimary carePsychiatryMajor depressive disorderDepression (economics)MedicineAnxiety disorderClinical psychologyPsychologyFamily medicineMood

Abstract

fetched live from OpenAlex

BACKGROUND: Major depressive disorder (MDD) and anxiety disorders are common and result in considerable suffering and economic loss. People suffering from major depressive disorder and/or anxiety disorders are commonly encountered in the primary care setting. Unfortunately, most people with these disorders remain either untreated or inadequately treated; current data suggest that general practitioners fail to diagnose up to half of cases of major depressive disorder or anxiety. There is a need for screening tools that will help physicians and other professionals in primary care recognize and adequately treat major depressive disorder and anxiety disorders. While the currently-available self-report screening instruments have been demonstrated to be reliable and valid, there remain considerable barriers to their widespread use in primary care. OBJECTIVE: The purpose of the present study is to report preliminary validation data for a freely-available, brief, Web-based, self-report screener for major depressive disorder and anxiety disorders. METHODS: The Web-Based Depression and Anxiety Test (WB-DAT) was administered to 193 subjects who presented for assessment and/or treatment in ongoing research projects being conducted at the Mood and Anxiety Program and Clinical Research Department at the Centre for Addiction and Mental Health in Toronto, Ontario, Canada. Subjects completed the Web-based screening instrument and were subsequently interviewed with the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) Axis I Disorders (SCID-I/P). The diagnostic data from the screening instrument were then compared with the data from the individual's SCID-I/P interview. Diagnostic concordance between SCID-I/P diagnoses and the Web-Based Depression and Anxiety Test were assessed using Cohen's kappa, sensitivity, specificity, positive predictive value, negative predictive value, and efficiency. RESULTS: Agreement ranged from acceptable to good (0.57-0.70) for major depressive disorder, panic disorder with and without agoraphobia (PD+/-AG), social phobia/social anxiety disorder, obsessive compulsive disorder (OCD), generalized anxiety disorder (GAD), and post traumatic stress disorder (PTSD). With the exception of generalized anxiety disorder, the sensitivity (0.71-0.95) and specificity (0.87-0.97) for the major diagnostic categories assessed by the Web-Based Depression and Anxiety Test were good. The sensitivity for generalized anxiety disorder was somewhat lower (0.63) but acceptable. Positive predictive values were good (0.60-0.75) for major depressive disorder, obsessive compulsive disorder, generalized anxiety disorder, and post traumatic stress disorder, and acceptable for panic disorder with and without agoraphobia and for social phobia/social anxiety disorder. CONCLUSIONS: These preliminary data suggest that the Web-Based Depression and Anxiety Test is reliable for identifying patients with and without major depressive disorder and the anxiety disorders of panic disorder with and without agoraphobia, social phobia/social anxiety disorder, obsessive compulsive disorder, and post traumatic stress disorder. Further research is required in a larger sample in primary care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.073
GPT teacher head0.464
Teacher spread0.392 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations101
Published2003
Admission routes2
Has abstractyes

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