Validation of the Malay Self-Report Quick Inventory of Depressive Symptomatology in a Malaysian Sample
Bibliographic record
Abstract
Depression is ranked as the second-leading cause for years lived with disability worldwide. Objective monitoring with a standardized scale for depressive symptoms can improve treatment outcomes. This study evaluates the construct and concurrent validity of the Malay Self-Report Quick Inventory of Depressive Symptomatology (QIDS-SR16) among Malaysian clinical and community samples. This cross-sectional study was based on 277 participants, i.e., patients with current major depressive episode (MDE), n = 104, and participants without current MDE, n = 173. Participants answered the Malay QIDS-SR16 and were administered the validated Malay Mini-International Neuropsychiatric Interview (MINI) for DSM-IV-TR. Factor analysis was used to determine construct validity, alpha statistic for internal consistency, and receiver operating characteristic (ROC) analysis for concurrent validity with MINI to determine the optimal threshold to identify MDE. Data analysis provided evidence for the unidimensionality of the Malay QIDS-SR16 with good internal consistency (Cronbach’s α = 0.88). Based on ROC analysis, the questionnaire demonstrated good validity with a robust area under the curve of 0.916 (p < 0.000, 95% CI 0.884−0.948). A cut-off score of nine provided the best balance between sensitivity (88.5%) and specificity (83.2%). The Malay QIDS-SR16 is a reliable and valid instrument for identifying MDE in unipolar or bipolar depression.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".