Validating the Beck Depression Inventory-II for Hong Kong Community Adolescents
Bibliographic record
Abstract
The primary purpose of this study was to test for the validity of a Chinese version of the Beck Depression Inventory-II (C-BDI-II) for use with Hong Kong community (i.e., nonclinical) adolescents. Based on a randomized triadic split of the data (N = 1460), we conducted exploratory factor analysis on Group1 (n = 486) and confirmatory factor analysis (CFA) within the framework of structural equation modeling on Groups 2 (n = 487) and 3 (n = 487); the second CFA served as a cross-validation of the determined factor structure. Factor analytic results, based on a 4-factor structure that comprised 1 2nd-order general factor of Depression and 3 1st-order factors representing Negative Attitude, Performance Difficulty, and Somatic Elements, replicated those reported previously for Canadian (Byrne & Baron, 1993), Swedish (Byrne, Baron, Larsson, & Melin, 1995), and Bulgarian (Byrne, Baron, & Balev, 1998) nonclinical adolescents. Based on this cross-validated factor structure, findings related to internal consistency reliability, stability over a 6-month time lag, and relations with relevant external criteria provided strong support for the valid use of the C-BDI-II in measuring depressive symptoms for Hong Kong community adolescents.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".