A study on patient satisfaction with psychiatric services
Bibliographic record
Abstract
CONTEXT: Patient satisfaction with psychiatric services is an important construct, which influences multiple areas including treatment adherance and outcome. There is paucity of studies from India in this area. AIMS: To measure satisfaction of psychiatric outpatients of Mental Health Institute, Cuttack, and to assess the relationship between demographic and clinical variables with different domains of patient satisfaction. SETTINGS AND DESIGN: In a cross-sectional study, patients of age 18-60 years receiving treatment for at least six months from outpatient department of Mental Health Institute, SCB Medical College, Cuttack, were assessed. MATERIALS AND METHODS: Participants (n=60) were selected randomly. Satisfaction was assessed by Patient Satisfaction Questionnaire-18 (PSQ-18) and the diagnoses were based on the Diagnostic and Statistical Manual-IV-TR. Severity of psychiatric disorder was measured using Clinical Global Impression-severity scale. The functioning level was assessed by Global Assessment of Functioning. STATISTICAL ANALYSIS: Continuous variables compared using independent t-test or analysis of variance. RESULTS: Diagnostic break-up of the patients included anxiety disorder (36.6%), major depressive disorder (30.0%), bipolar disorder (16.6%), and schizophrenia (16.6%). Greatest level of satisfaction was noted in interpersonal aspects (71.4%) and time spent with doctors (62.4%). General satisfaction level was a little over 50% (57%). Differences between composite scores of PSQ-18 in the diagnostic categories were statistically significant; patients with depression had the highest score followed by those with anxiety disorder, bipolar, and the least was with schizophrenia patients. There was variability among the demographic and clinical groups regarding the various subscale score of PSQ-18. CONCLUSIONS: Satisfaction of psychiatric outpatients was observed to be different in various clinical and demographic groups and across many domains of satisfaction.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".