Needs of Patients with Schizophrenia and Their Predictors
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore the needs of patients with schizophrenia and to determine the sociodemographic and clinical variables associated with these needs. METHOD: The study was carried out with 94 patients diagnosed with schizophrenia and their primary caregivers. The diagnoses were established based on DSM-IV-TR (American Psychiatric Association 1994) criteria. The patients' needs were evaluated with the Camberwell Assessment of Needs Scale (CAN), which reflects both the patient and the caregiver views on needs and problems. In addition, the Positive and Negative Syndrome Scale, Calgary Depression Scale for Schizophrenia, the Personal and Social Performance Scale, the Quality of Life Scale for Schizophrenia and the Perceived Family Burden Scale were used as data collection tools. RESULTS: 'Psychotic symptoms' stood out as the most prevalent need to be met. 'Money' and 'intimate relations' were determined as the needs that the patients suffered most from. Being female, being married, living with families, having high school or higher education were found less associated with the needs. The variables explained 45.1% of the variance in predicting needs and 23.4% in predicting unmet needs. The Calgary Depression Scale for Schizophrenia and Perceived Family Burden Scale were significant predictors for patients and the PANSSPositive Symptoms Scale for caregivers. CONCLUSION: Any treatment targeting solely the symptoms proves to be insufficient for patients with schizophrenia. Treatment planning should focus on the needs of patients and the needs identified by patients should be at the center of the treatment. A 'needs assessment' will make an important contribution not only to the planning of individual treatments but also to the planning of a community mental health services scheme and increasing its effectiveness.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| 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".