Associations between negative and positive automatic thoughts and clinical variables in patients with schizophrenia
Bibliographic record
Abstract
This study investigated the relationships between negative and positive automatic thoughts and clinical variables in patients with schizophrenia. The participants included 36 patients with schizophrenia (male = 16; female = 20; age = 42.86 ± 9.40) who were outpatients in the Department of Psychiatry at Tokushima University Hospital. We used the Automatic Thoughts Questionnaire-Revised (ATQ-R), Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia (CDSS), and Brief Assessment of Cognition in Schizophrenia (BACS) to assess negative and positive automatic thoughts, positive and negative symptoms, depressive symptoms, and neurocognition, respectively. Spearman rank correlation coefficients were calculated to determine the relationships between negative and positive automatic thoughts and clinical variables. No relationship was observed between negative and positive automatic thoughts. Negative automatic thoughts were related to depressive symptoms. Positive automatic thoughts were related to neurocognition. We therefore surmise that each automatic thought might have different clinical features and outcomes, and should therefore be treated accordingly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".