Effect of an Institutional Medication Adherence Program for Long-Acting Injectable Risperidone on Adherence and Psychiatric Hospitalizations: Evidence From a Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Long-acting injectable (LAI) atypical antipsychotics are associated with improved adherence and reduced relapse rates in schizophrenia but reminder-based interventions may further improve outcomes. OBJECTIVES: To assess an institutional medication adherence program's (IMAP) effectiveness on adherence and psychiatric hospitalizations among schizophrenia patients taking risperidone LAI (RLAI). METHODS: Between 2009 and 2010, we recruited patients meeting DSM-IV criteria for schizophrenia treated with RLAI receiving outpatient care from psychiatric centres in France. The IMAP consisted of calling patients 48 hours prior to their scheduled RLAI injections and within 3 days of a missed appointment. Centres applying the IMAP to ≥50% of scheduled patient injections were deemed compliant. Patients were followed up to one year for adherence (≥80% of scheduled RLAI injections received within 5 days of the scheduled date) and psychiatric hospitalizations. RESULTS: Among 506 patients recruited from 36 centres, the hospitalization rate was 32.5 per 100 person-years. 15 centres treating 243 patients were IMAP compliant and 21 centres treating 263 patients were not. IMAP compliance was associated with lower psychiatric hospitalization rates (crude RR: 0.64 [95% CI: 0.44-0.93]; adjusted RR: 0.78 [95% CI: 0.47-1.27]). Nearly 75% of patients were adherent to RLAI. While patient adherence had little impact on hospitalization rates (adjusted RR: 0.92 [95% CI: 0.59-1.44]), IMAP compliance was more effective among non-adherent (adjusted RR: 0.45 [95% CI: 0.16-1.28]) than adherent (adjusted RR: 0.88 [95% CI: 0.51-1.53]) patients. CONCLUSIONS: IMAPs may improve patient adherence and reduce psychiatric hospitalizations, particularly among patients with difficulties adhering to LAI antipsychotics.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".