The Reliability and Validity of the Calgary Depression Scale for Schizophrenia
Bibliographic record
Abstract
Objective To explore the reliability and validity of the Mandarin version of the Calgary Depression Scale for Schizophrenia (CDSS). Methods 100 healthy controls redid the CDSS in 2 weeks for investigating the re-test reliability; CDSS, Hamilton Depression Scale (HAMD), Positive and Negative Syndrome Scale (PANSS) were evaluated in 150 schizophrenic patients, who were separated into schizophrenia with or without depression group by Structured Clinical Interview for DSM-IV-TR (Research Version, SCID), to explore the internal consistency, split-half reliability, criteria-related validity, convergent and discriminant validity. Results The re-test reliability of CDSS was good (r=0.650, P0.01); the CDSS was found to have high internal consistency (α=0.838, P0.01) and split-half reliability (r=0.857, P0.01). The criteria-related validity was excellent (Z=-5.109, P0.01); the correlation between CDSS and HAMD was significantly positive (r=0.751, P0.01), a weak correlation between CDSS and the PANSS negative and positive subscales was also discovered (r=0.23, P0.05). Conclusions Our findings suggest positive support to Mandarin version of the CDSS; it may be helpful to identify the depressive symptoms in Chinese schizophrenic patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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".