Predictors of Self-Reported Antidepressant Adherence
Bibliographic record
Abstract
The authors' objectives of this research were: (1) to assess levels of selfreported antidepressant adherence and reasons for nonadherence and (2) to investigate determinants of nonadherence. A group of general hospital and community psychiatry practice mood disorder outpatients (n=80) took a self-report questionnaire that assessed beliefs about antidepressants, self-efficacy, and reasons for nonadherence. High levels of adherence were reported: 58 patients (73%) indicated they took their medication as directed more than 80% of the time. Practical issues (e.g., simply forgetting or a change in routine) were the most frequently identified reasons for nonadherence. Patients were more likely to report nonadherence if they experienced a sexual side effect, had lower self-efficacy, were female, and had not completed post-secondary education. Clinicians should be cognizant of this complexity and address not only issues related to medication efficacy and tolerability, but also social mediators and health beliefs when prescribing antidepressants.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".