Factor structure of the BDI-II in Parkinson’s disease.
Bibliographic record
Abstract
OBJECTIVE: There is substantial heterogeneity in depressive symptomology for individuals with Parkinson's disease (PD). It is unknown whether the Beck Depression Inventory-Second Edition (BDI-II) is capable of identifying such phenotypic variations of depression. METHOD: We investigated the factor structure of the BDI-II and its associations with demographic characteristics and other nonmotor symptoms in PD. We reviewed the cases of 236 patients with a confirmed PD diagnosis. Evaluations included the BDI-II, Montreal Cognitive Assessment (MoCA), Apathy Scale (AS), and Geriatric Anxiety Inventory (GAI). We used exploratory structural equation modeling (ESEM) with target rotations as this method integrates aspects of exploratory and confirmatory factor analysis. We conducted hierarchical regressions to assess for associations between the BDI-II factors and gender, age, education, disease duration, cognition, anxiety, and apathy. RESULTS: ESEM supported the retention of a Somatic factor and an Affective factor that accounted for 53% of the model variance. Model goodness-of-fit measures were within normal limits. Higher AS scores were positively associated with the Somatic and Affective factors. Higher GAI scores were positively associated only with the Affective factor. There were no other significant relationships with factor scores. CONCLUSIONS: This study supports the retention of a two-factor model of the BDI-II in PD. These unique clusters of depressive symptoms in PD can be used to guide clinical decisions about the need for further psychiatric evaluation and the appropriateness of different therapeutic interventions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".