Increasing medication adherence and income assistance access for first-episode psychosis patients
Bibliographic record
Abstract
BACKGROUND: Assertive community treatment for first-episode psychosis programs have been shown to improve symptoms and reduce service use. There is little or no evidence on whether these programs can increase access to income assistance and improve medication adherence in first episode psychosis patients. This research examines the impact of the Early Psychosis Prevention and Intervention Service (EPPIS) on these outcomes. METHODS: We extracted data on EPPIS patients held in the Data Repository at the Manitoba Centre for Health Policy. The Repository is a comprehensive collection of person-level de-identified administrative records, including data from Manitoba's health services. We compared income assistance use and antipsychotic medication adherence in EPPIS patients to a historical cohort matched on pattern of diagnosis. Confounders were adjusted through propensity-score weighting with asymmetrical trimming. Odds ratios (OR), hazard ratios (HR) and 95% confidence intervals were calculated. RESULTS: We identified a matched sample of 244 patients and 449 controls. EPPIS patients had a higher rate of income assistance use during the program (67·4% vs. 38·7%; p< 0·0001). EPPIS patients were more likely to have been prescribed at least one antipsychotic medication than the control cohort, both during the program (OR = 15·05; 95%CI 10·81 to 20·94) and after the program ended (OR = 5·20; 95%CI: 4·50 to 6·02). Patients in EPPIS were also more likely to adhere to their medication during the program (OR = 4·71; 95%CI 3·75 to 5·92), and after the program (OR = 2·54; 95%CI 2·04 to 3·16). CONCLUSION: Enrolment in the EPPIS program was associated with increased adherence to antipsychotic medication treatment and improved uptake of income assistance.
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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 | 0.001 | 0.000 |
| 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".