Exploring the Barriers to and Facilitators of Using Evidence-Based Drugs in the Secondary Prevention of Cardiovascular Diseases: Findings From a Multistakeholder, Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Health-system barriers and facilitators associated with cardiovascular medication adherence have seldom been studied, particularly in low- and middle-income countries where uptake rates are poorest. OBJECTIVES: This study sought to explore the major obstacles and facilitators to the use of evidence-supported medications for secondary prevention of cardiovascular disease using qualitative analysis in 2 diverse countries across multiple levels of their health care systems. METHODS: A qualitative descriptive study approach was implemented in Hamilton, Ontario, Canada, and Delhi, India. A purposeful sample (n = 69) of 23 patients, 10 physicians, 2 nurse practitioners, 5 Department of Ayurveda, Yoga and Naturopathy, Unani, Siddha, and Homoeopathy physicians, 11 pharmacists, 3 nurses, 4 hospital administrators, 1 social worker, 3 nongovernmental organization workers, 2 pharmaceutical company representatives, and 5 policy makers participated in interviews in Hamilton, Ontario, Canada (n = 21), and Delhi, India (n = 48). All interviews were digitally recorded and transcribed followed by directed content analysis to summarize and categorize the interviews. RESULTS: Themes that emerged across the stakeholder groups included: medication counseling; monitoring adherence; medication availability; medication affordability and drug coverage; time restrictions; and task shifting. The depth of verbal medication counseling provided varied substantially between countries, with prescribers in India unable to convey relevant information about drug treatments due to time constraint and high patient load. Canadian patients reported drug affordability as a common issue and very few patients were familiar with government subsidized drug programs. In India, patients purchased medications out-of-pocket from private, community pharmacies to avoid long commutes, lost wages, and unavailability of medications from hospitals formularies. Task shifting medication-refilling and titration to nonphysician health workers was accepted and supported by physicians in Canada but not in India, where many of the physicians considered a high level of clinical expertise a precondition to carry out these tasks skillfully. CONCLUSIONS: Our findings reveal context-specific, health system factors that affect the patient's choice or ability to initiate and/or continue cardiovascular medication. Strategies to optimize cardiovascular drug use should be targeted and relevant to the health care system.
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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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".