The Influence of Perceived Social Support on Medication Adherence in First-Episode Psychosis
Bibliographic record
Abstract
OBJECTIVE: Our study examines the unique influence of social and family support on adherence to medication in a sample of patients treated for first-episode psychosis (FEP). METHOD: Social and family support using the Multidimensional Scale of Perceived Social Support and medication adherence (consensus of subjective and objective data) were evaluated on a monthly basis during a 6-month period in a sample of 82 FEP patients. The relation between social support and adherence was evaluated using correlational and linear regression analyses, controlling for other relevant variables. A longitudinal analysis using hierarchical linear models was conducted to model change in adherence over time. RESULTS: Monthly correlations between social support and adherence were significant at 4 of 7 time points during a 6-month period. There was a modest correlation between the percentage of months of good adherence and the average level of family support across the study period. The linear regression failed to demonstrate a significant relation between baseline social support and overall adherence during the entire study period. Change in social support over time was inversely associated with change in adherence. CONCLUSIONS: Our study emphasizes the concurrent influence of social (mostly family) support on adherence but this effect does not persist over time. Changes in the degree of social support may have a complex effect on changes in adherence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".