Validating the Children’s Depression Inventory-2: Results from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) study
Bibliographic record
Abstract
Childhood-onset depression has adverse consequences that are sustained into adulthood, which increases the significance of detection in early childhood. The Children's Depression Inventory (CDI) is used globally in evaluating depressive symptom severity in adolescents, and its second version, the CDI-2, was developed by taking into account advances in childhood depression research. Prior research has reported inconsistencies in its factor structure across populations. In addition, the CDI-2 has not yet been empirically validated with Southeast Asian populations. This study sought to empirically validate the CDI-2's psychometric properties and evaluate its factorial structure with a Singaporean community sample of non-clinical respondents. A total sample of 730 Singaporean children aged between 8.5 and 10.5 years was used. Psychometric properties of the CDI-2, including internal consistency as well as convergent and discriminant validity, were assessed. Factor analyses were conducted to assess the developers' original two-factor structure for a Southeast Asian population. This two-factor structure was not supported in our sample. Instead, the data provided the best fit for a hierarchical two-factor structure with factors namely, socio-emotional problems and cognitive-behavioural problems. This finding suggests that socio-cultural and demographic elements influence interpretation of depressive symptoms and therefore the emerging factor structure of the construct under scrutiny. This study highlights the need to further examine the CDI-2 and ensure that its interpretation is culture-specific. More qualitative work could also bring to light the idiosyncratic understanding of depressive symptomatology, which would then guide culture-specific validation of the CDI-2.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".