Early Predictors of Nonadherence to Antipsychotic Therapy in First-Episode Psychosis
Bibliographic record
Abstract
OBJECTIVE: To examine the hypothesis that poorer social and family support, identifiable at the onset of treatment, is associated with nonadherence in the first 6 months of treatment of patients with first-episode psychosis (FEP), independent of other patient-related factors. METHOD: Consecutive patients (n = 100) admitted to a specialized early intervention service for FEP who met the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, criteria for either a schizophrenia spectrum disorder or an affective psychosis were evaluated monthly for 6 months regarding their adherence to medications. Using sociodemographic and illness-related factors, including social and family support, as independent variables and adherence as the dependent variable, univariate analyses were followed by logistic regression. RESULTS: Fifty-six patients (54.9%) were adherent (76% to 100% of doses taken) and 46 (45.1%) nonadherent (less than 76% of doses taken). Nonadherent patients were less likely to have received a good level of social support (chi (2) = 5.89, df = 1, P = 0.02), as rated by their respective case manager, and more likely to be single (Fisher exact test, P = 0.019) and to have refused medication at the first offer of treatment (chi (2) = 19.70, df = 1, P = 0.001). Using logistic regression, both the level of social support (OR = 3.552, P = 0.03) and early medication acceptance (OR = 11.092, P < 0.001) were significant as predictors of adherence. CONCLUSION: These results suggest the significance of social and family support in achieving adherence to medications very early in the course of treatment of FEP, in addition to the influence of early acceptance or rejection of medication.
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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 | 0.010 |
| 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 | 0.000 |
| 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".