Predictors of treatment satisfaction in antipsychotic-naïve and previously medicated patients with acute-phase psychosis
Bibliographic record
Abstract
Background: Treatment satisfaction predicts treatment adherence and long-term outcome for patients with psychosis. It is therefore important to understand the underpinnings of patient satisfaction in psychosis treatment for optimal treatment delivery.Aims: To examine the associations between satisfaction and level and change in positive symptoms, insight, depression and side effects of antipsychotics in previously medicated and antipsychotic-naïve patients.Method: Data derive from a randomised trial, with 226 respondents at baseline and 104 at follow-up. The measures were the positive subscale and insight item from the Positive and Negative Syndrome Scale, Calgary Depression Scale, the UKU Consumer Satisfaction Rating Scale, and the UKU side effects scale. Structural equation modelling was used to test the model. The full information maximum likelihood estimator used all available data.Results: In the sample of 226 patients, 67.3% were male and 44.2% were antipsychotic-naïve. The mean age was 34.1 years. For previously medicated patients, satisfaction was predicted by level of insight (b = −2.21, β = −0.42) and reduction in positive symptoms (b = −0.56, β = −0.39). For antipsychotic-naïve patients, satisfaction was predicted by level and change of insight (b = −2.21, β = −0.46), change in depression (b = −0.37, β = −0.26) and side effects (b = −0.15, β = −0.30). All predictors were significant at the 0.05 level.Conclusion: Reducing positive symptoms and side effects are important to enhance patient satisfaction. However, improving insight and reducing depression are more important in antipsychotic-naïve patients.Trial registration: ClinicalTrials.gov identifier: NCT00932529.
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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) | 0.001 | 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".