Frequency of outpatient follow-up application in schizophrenia: community mental health center research
Bibliographic record
Abstract
Objective: In this study, we aimed to identify factors that affect the frequency of application to community mental health center in patients with schizophrenia. Methods: Sixty-four patients who had been diagnosed with schizophrenia by DSM-IV-TR diagnostic criteria and followed up at least 6 months by one of three community mental health centers depending on Bakirkoy Psychiatric and Neurological Diseases Training and Research Hospital were enrolled in our study. 41 patients were frequently callers, 23 patients were coming in rarely. Sociodemographic data form, Positive and Negative Syndrome Scale, Calgary Depression Scale for Schizophrenia, Global Assessment Scale, Social Functioning Scale were completed by clinicians in face-to-face patient interviews. Results: No significant difference was found in the socio-demographic characteristics between the two groups. The number of hospitalizations was significantly higher in the rare applicant group. In addition, the number of patients who have been refused treatment, have been treated at home and with depressive symptoms was significantly higher in the rare applicant group than in the other group. On the other hand, attending rates at the psychoeducation group meetings and social skills training were significantly higher in the frequent applicant group. Global assessment scale scores were significantly higher in frequently calling patients. Conclusion: The frequency of visits to the community mental health center is an important determinant of the clinical outcome of patients. We found that the global functioning levels, depressive symptoms, number of hospitalizations, attending to the psychoeducation group meetings and social skills lessons are determinative for application rates.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".