Clinical Correlates of Treatment Adherence and Insight in Patients with Schizophrenia
Bibliographic record
Abstract
Aim: The aim of this study was to assess clinical correlates of treatment adherence and insight in patients with schizophrenia. Methods: That cross-sectional study included 229 outpatients with schizophrenia who were admitted to the Psychiatry Outpatient Clinic of Health Sciences University Dışkapı Yıldırım Beyazıt Training and Research Hospital. All participants were administered a socio-demographic form, Morisky Medication Adherence Questionnaire, Schedule for Assessing the three components of insight, Brief Psychiatric Rating Scale, Positive and Negative Symptoms Rating Scale, Calgary Depression Scale for Schizophrenia, and Global Assessment of Functioning Scale. Collected data were analyzed with descriptive statistics, Pearson Correlation Analysis, and logistic regression analysis. Results: Poor treatment adherence was associated with male gender, lower insight level, more severe psychotic symptoms, and lower functionality level. The insight score was negatively correlated with the severity of psychotic symptoms, duration of the disorder, and mean antipsychotic dose; but positively correlated with advanced age of onset and higher functionality level. The logistic regression analysis revealed that functionality level was more predictive of poor medication adherence. Conclusion: Poor treatment adherence and lower insight level were closely associated with more severe clinical symptoms and lower functionality level. It was noteworthy that adherence and insight levels both showed a high predictivity for the wellbeing of the patients. Therefore, psychotherapeutic interventions should be implemented to increase treatment adherence and insight in schizophrenia even if the psychotic symptoms show resistance. Further research is needed to clarify clinical associations of the treatment adherence and insight level in patients with schizophrenia.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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 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".