Direct and indirect predictors of medication adherence by adults with bipolar disorder
Bibliographic record
Abstract
Introduction Medication adherence by persons with bipolar disorder (BD) is inconsistent. This is disconcerting, as BD is treatment responsive, side-effects are few, and the impact of both hypo/manic and depressive mood episodes can be considerable (e.g., self-harm). Objectives For this study, we computed a path model to identify both direct and indirect predictors of medication adherence. This included both clinical and psychosocial independent variables (e.g., BD symptoms, psychological well-being, alcohol misuse). Methods From the BADAS (Bipolar Affective Disorder and older Adults) Study, we identified a global sample of adults with the BD. Participants were recruited using microtargeted, Facebook advertising. This sample included persons living in Canada, U.S., U.K., Ireland, Australia and New Zealand (M = 55.35 years, SD = 9.65). Results Direct predictors included perceived cognitive failures and alcohol misuse. Of note, medication adherence is inversely associated with number of prescribed antipsychotic medications. Neither symptoms of depression nor hypo/mania emerged as direct predictors of medication adherence. Similarly, psychological well-being appears indirectly associated with adherence (via BD symptoms). Conclusions Despite the wide age range of participants (22 – 73 years), age did not emerge as a predictor of adherence. Nor do cognitive failures appear significantly associated with age suggesting that both young and older adults with BD perceived cognitive loss. Disclosure No significant relationships.
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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 | 0.001 |
| 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) | 0.010 | 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".