Reliability and validity of the Children's Depression Inventory–Japanese version
Bibliographic record
Abstract
BACKGROUND: Depression has major negative consequences for individuals and society, and psychological assessment tools for early disease detection are needed. The aim of this study was to investigate the reliability and validity of an updated Japanese version of the Children's Depression Inventory (CDI-J) and set a cut-off score for the detection of depression. METHODS: The participants consisted of 465 children and adolescents aged 7-17 years. The control (CON) groups consisted of students recruited from elementary and junior-high school (CONEJ) and children recruited from among hospital staff members (CONRE), while the outpatient clinical (OPC) groups consisted of pediatric psychosomatic outpatients (OPCPD) and adolescent psychiatric outpatients (OPCPS). The CON and OPC CDI-J scores underwent factor analysis using varimax rotation, followed by measurement invariance analysis. The Youth Self-Report (YSR) was administered to assess concurrent validity. The Mini-International Neuropsychiatric Interview was administered to the OPC group to diagnose current depressive symptoms. Receiver operating characteristics (ROC) analysis was conducted to evaluate case-finding performance and to set cut-off points for the detection of depression. RESULTS: The CDI-J was reliable in terms of internal consistency (Cronbach α = 0.86; mean inter-item correlation, 0.16). Re-test reliability was substantial (mean interval 18 days: γ = 0.59, P < 0.05). The four-factor solution exhibited adequate internal consistency (range, 0.52-0.73) and correspondence (Pearson correlation of 0.65 with the YSR) for both the CON and OPC groups. On ROC analysis the optimal cut-off score was 23/24. CONCLUSION: The CDI-J can be used as a reliable and well-validated instrument alongside standard diagnostic procedures.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".