DSM-5 Field Trials in the United States and Canada, Part I: Study Design, Sampling Strategy, Implementation, and Analytic Approaches
Bibliographic record
Abstract
OBJECTIVE: This article discusses the design,sampling strategy, implementation,and data analytic processes of the DSM-5 Field Trials. METHOD: The DSM-5 Field Trials were conducted by using a test-retest reliability design with a stratified sampling approach across six adult and four pediatric sites in the United States and one adult site in Canada. A stratified random sampling approach was used to enhance precision in the estimation of the reliability coefficients. A web-based research electronic data capture system was used for simultaneous data collection from patients and clinicians across sites and for centralized data management.Weighted descriptive analyses, intraclass kappa and intraclass correlation coefficients for stratified samples, and receiver operating curves were computed. The DSM-5 Field Trials capitalized on advances since DSM-III and DSM-IV in statistical measures of reliability (i.e., intraclass kappa for stratified samples) and other recently developed measures to determine confidence intervals around kappa estimates. RESULTS: Diagnostic interviews using DSM-5 criteria were conducted by 279 clinicians of varied disciplines who received training comparable to what would be available to any clinician after publication of DSM-5.Overall, 2,246 patients with various diagnoses and levels of comorbidity were enrolled,of which over 86% were seen for two diagnostic interviews. A range of reliability coefficients were observed for the categorical diagnoses and dimensional measures. CONCLUSIONS: Multisite field trials and training comparable to what would be available to any clinician after publication of DSM-5 provided “real-world” testing of DSM-5 proposed diagnoses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.261 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".