Assessing negative symptoms in schizophrenia: Validity of the clinical assessment interview for negative symptoms in Singapore
Bibliographic record
Abstract
This study aimed to examine the validity of the Clinical Assessment Interview for Negative Symptoms (CAINS) in Singapore. 274 participants with schizophrenia were assessed on the CAINS, Scale for the Assessment of Negative Symptoms (SANS), Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia (CDSS), Social and Occupational Functioning Assessment Scale (SOFAS) and the Simpson-Angus Extrapyramidal Side Effects Scale (SES). Factor analyses were conducted and Cronbach's coefficient alpha was calculated. Spearman's correlation coefficient was used to assess correlations. The 2-factor model of the CAINS failed to fit our data. Exploratory factor analysis of a randomly selected split-half of the sample yielded four factors: motivation-pleasure (MAP) social, MAP vocational, MAP recreational and expression (EXP), accounting for 73.94% of the total variance. Confirmatory factor analysis on the remaining sample supported this factor structure. Cronbach's alpha for the CAINS was 0.770. Significant correlations were observed between the CAINS total and the SANS total and PANSS negative subscale scores. Good divergent validity was shown by insignificant correlations with PANSS positive subscale score and CDSS total score. The MAP social and recreational factor scores had moderate correlations with the SANS anhedonia-asociality subscale scores, whereas the MAP vocational factor had the highest correlation with the avolition-apathy subscale of the SANS. EXP factor score correlated strongly with the SANS affective flattening and alogia subscales scores. In conclusion, the CAINS has good psychometric properties and can be used by clinicians to assess negative symptoms in individuals with schizophrenia in the local population.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".