Prevalence of Substance Use in Patients Diagnosed with Schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Substance abuse among schizophrenic patients is a growing clinical concern. Substance use disorders and their effects on the course of schizophrenia have made the identification and treatment of schizophrenic patients a high priority. This study aimed to investigate the prevalence of substance use, preferred types of substances, sociodemographic characteristics and clinical features of schizophrenia, and substance use impact in schizophrenic patients. METHODS: Hundred patients who were consecutively admitted to the psychiatry clinic and were diagnosed with schizophrenia according to the DSM-IV criteria were enrolled in this study. Individual interviews were conducted during the patients. In order to evaluate substance abuse disorder (SAD) as per DSM-IV criteria, the substance use disorder section of the structured clinical interview for DSM disorders-II (SCID-II) form was used. In addition, the following were applied to schizophren-ic patients: sociodemographic data form, medical history form, Brief Disability Questionnaire (BDQ), UKU Side Effect Rating Scale (UKUSERS), Insight Rating Scale (IRS), Alcohol Use Dis-orders Identification Test (AUDIT), Fagerstrom Nicotine Dependence Test (FNDT), Global As-sessment of Functioning Scale (GAF), Scale for the Assessment of Positive Symptoms (SAPS), Scale for the Assessment of Negative Symptoms (SANS), and Calgary Depression Scale (CDS). RESULTS: Schizophrenia and alcohol and drug use were more common in males, and younger age was found to have no association with substance use. Unemployment, low education levels, rural survival rates, age at disease onset, the doctor first age of the applicant, the first inpatient years, legal issues, harm caused by others and suicidal behavior, SAPS, SANS, CDS received from their scores significant difference was detected. Schizophrenic patients with substance use had higher side effects of drugs, disability, and psychopathology scores than schizophrenic patients without substance use. The functioning of schizophrenic patients with substance use was worse, and the total length of stay was longer. Nicotine, alcohol, biperiden, cannabis, and volatile substances were the preferred materials most commonly used by schizophrenic patients. CONCLUSION: In our country, limited research has been conducted on the prevalence of substance use in schizophrenic patients. Therefore, we believe that this study will contribute to the literature on the subject. More sample groups and first-episode patients as well as follow-up studies will contribute to a better understanding of the effect of substance use on the clinical course of schizophrenia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".