The latent symptom structure of the Beck Depression Inventory–II in outpatients with major depression.
Bibliographic record
Abstract
The Beck Depression Inventory-II (BDI-II) is a self-report instrument frequently used in clinical and research settings to assess depression severity. Although investigators have examined the factor structure of the BDI-II, a clear consensus on the best fitting model has not yet emerged, resulting in different recommendations regarding how to best score and interpret BDI-II results. In the current investigation, confirmatory factor analysis was used to evaluate previously identified models of the latent symptom structure of depression as assessed by the BDI-II. In contrast to previous investigations, we utilized a reliably diagnosed, homogenous clinical sample, composed only of patients with major depressive disorder (N = 425)--the population for whom this measure of depression severity was originally designed. Two 3-factor models provided a good fit to the data and were further evaluated by means of factor associations with an external, interviewer-rated measure of depression severity. The results contribute to a growing body of evidence for the Ward (2006) model, including a General (G) depression factor, a Somatic (S) factor, and a Cognitive (C) factor. The results also support the use of the BDI-II total scale score. Research settings may wish to model minor factors to remove variance extraneous to depression where possible.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".