Factors influencing medication adherence among hypertensive patients: A qualitative study
Bibliographic record
Abstract
Abstract Background: Medication non-adherence is a significant challenge in managing chronic diseases like hypertension. It often results in poor health outcomes and increased healthcare costs. Therefore, understanding patients' perspectives on barriers to adherence is vital in developing realistic interventions to promote medication adherence in hypertension and similar chronic diseases. Aim: This qualitative phenomenological study aimed to explore the experiences and barriers to adherence among hypertensive patients at the Bamenda Regional Hospital in Cameroon. Methods: Semi-structured interviews and a focus group discussion were conducted with 10 hypertensive patients aged 30 years and above, having difficulties adhering to their treatment plan. The interviews were transcribed and analyzed by thematic analysis using NVivo software. Results: Several key factors hindering adherence were identified from the themes including limited knowledge and awareness about hypertension and its treatment, negative attitudes and perceptions towards hypertension management, socioeconomic constraints limiting access to care, lack of social support, and poor patient-provider relationships. The study revealed a predominance of the biomedical model of care which often neglected patients' psychosocial needs. Conclusion: Addressing the barriers to adherence in hypertension patients demands a patient-centred approach to care. Such an approach must combine interventions for patient education and socioeconomic support. Social support systems, family involvement in care, and proper improved patient-provider collaboration also hold the potential to improve adherence. Recommendations: Based on the findings, this study recommends the implementation of targeted interventions such as patient education, socioeconomic support systems, family involvement in care, and training of care providers on patient-provider collaboration. Adopting a biopsychosocial care model is also crucial for promoting 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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".