Patient Perceived Barriers and Enablers to Medication Adherence in the Treatment of Depression: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Depression affects approximately 280 million individuals globally and it is a leading cause of disability. Despite effective medication options, 50% of patients prematurely discontinue antidepressants within 6 months. We sought to understand patients' perspectives regarding their needs and expectations related to antidepressants. OBJECTIVES: To identify and describe enablers and barriers that influence adult patients' medication adherence in depression treatment and to explore patients' educational needs on initiating or continuing antidepressant therapy. METHODS: Qualitative descriptive study was conducted using individual, semi-structured interviews of adult patients with depression who were prescribed an antidepressant within 3 months of study recruitment at an urban primary care clinic in Toronto, Canada. Thirteen participants were interviewed. Interviews were recorded and transcribed verbatim for inductive thematic analysis. RESULTS: Six themes emerged: safety and effectiveness of antidepressant, understanding of depression and its management, medication administration, healthcare experiences in the treatment of depression, and social influences and relationships. Barriers to adherence included adverse effects of antidepressants, preference for non-pharmacological therapies, uncertainty about therapeutic effects, and social stigma. In contrast, enablers were positive responses from antidepressants, fear of relapse, reminder aids, established routine, and a trusting patient-provider relationship. Participants desired access to reliable, evidence-based, and personalized educational information delivered through verbal, written, and digital formats to support antidepressant adherence. CONCLUSION: To overcome the identified barriers, educational strategies should involve both patients and their prescribers to identify patient-specific needs and treatment goals, engage in shared decision-making, and maintain consistent follow-up to support antidepressant 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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".