Insight and Associated Factors among Patients with Schizophrenia in Mental Specialized Hospital, Ethiopia, 2018
Bibliographic record
Abstract
BACKGROUND: Insight is the degree of the patient's awareness and understanding of their attributions, feelings, behavior and disturbing symptoms. Majority of the patients with schizophrenia have poor insight and insight is an important prognostic indicator in schizophrenia to enhance treatment compliances and reducing the risks of clinical deterioration. The main objective of this study was to assess insight and its associated factors among patients with schizophrenia at mental specialized hospital in Ethiopia. METHODS: Institutional based cross-sectional study was conducted from May to June 2018 Mental Specialized Hospital among 455 patients with schizophrenia. Insight was measured by an abridged version of Scale to assess unawareness of mental disorder. Positive and Negative Syndrome Scale, Calgary depressive scale, Oslo social support scale was used to identify factors associated with insight. Simple and multiple linear regression analysis were used to assess associated factors of insight in the participants. RESULTS: The mean score of insight was 13.66 (95% CI 13.27, 14.04). Age at first onset of illness, duration of treatments, depressive symptoms were inversely associated with mean insight score; whereas unemployed, positive and negative syndrome, previous hospitalization, >=2 episodes were positively associated with mean insight score. CONCLUSION: Nearly half of the study participants were scored above the mean insight score so, the clinicians and psychotherapists should have to work together to improve insight among patients with 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 | 0.001 |
| 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 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".