Case Manager- and Patient-Rated Alliance as a Predictor of Medication Adherence in First-Episode Psychosis
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the association between adherence to antipsychotic medication and working alliance (WA) ratings as reported separately by case manager (CM) and patient in first-episode psychosis (FEP) and to identify whether other factors previously related to adherence influence this relationship. METHODS: Adherence was evaluated every month in 81 participants who met criteria for a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, psychotic disorder (affective or nonaffective) and were treated in a specialized early intervention program. Adherence was measured, taking into account information from patient and clinician reports and pill counting. The WA, as assessed by both CM and patient, was assessed using the Working Alliance Inventory. RESULTS: The WA was stable during the course of the study as rated by both patient and CM. The "task" domain of WA was the subdomain most significantly correlated to adherence in cross-sectional analysis. The WA as measured by CM at study baseline was a significant predictor of the number of subsequent months with "good" adherence independently of other variables, including adherence at treatment onset (β = 0.011; P = 0.020; 95% confidence interval, 0.002-0.020). However, the WA as measured by patients was not similarly predictive of subsequent adherence (β = 0.003; P = 0.31; 95% confidence interval, -0.003 to 0.010). CONCLUSIONS: The CM-rated WA is a significant predictor of future medication adherence in FEP, suggesting that good alliance can improve adherence in this population.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".