Effect of a medication management training package for nurses on clinical outcomes for patients with schizophrenia
Bibliographic record
Abstract
BACKGROUND: Non-compliance attenuates the efficacy of treatments for physical and mental disorders. AIMS: To assess the effectiveness of a medication management training package for community mental health nurses (CMHNs) in improving compliance and clinical outcomes in patients with schizophrenia. METHOD: Pragmatic randomised controlled trial. Sixty CMHNs in geographical clusters were assigned randomly to medication management training or treatment as usual. Each CMHN identified two patients on their case-load who were assessed at baseline and again after 6 months by a research worker. The primary efficacy outcome of interest was psychopathology, measured using the Positive and Negative Syndrome Scale (PANSS). RESULTS: Nurses who had received medication management training produced a significantly greater reduction in patients'overall psychopathology compared with treatment as usual at the end of the 6-month study period (change in PANSS total scores: medication management -16.62, treatment as usual 1.17; difference -17.79; 95% CI -24.12 to -11.45; P<0.001). CONCLUSIONS: Medication management training for CMHNs is effective in improving clinical outcomes in patients with schizophrenia.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".