Mental health in hypertension: assessing symptoms of anxiety, depression and stress on anti-hypertensive medication adherence
Bibliographic record
Abstract
BACKGROUND: Patients with chronic conditions like hypertension may experience many negative emotions which increase their risk for the development of mental health disorders particularly anxiety and depression. For Ghanaian patients with hypertension, the interaction between hypertension and symptoms of anxiety, depression and stress remains largely unexplored. To fill this knowledge gap, the study sought to ascertain the prevalence and role of these negative emotions on anti-hypertensive medication adherence while taking into account patients' belief systems. METHODS: The hospital-based cross-sectional study involving 400 hypertensive patients was conducted in two tertiary hospitals in Ghana. Data were gathered on patient's socio-demographic characteristics, anxiety, depression and stress symptoms, spiritual beliefs, and medication adherence. RESULTS: Hypertensive patients experienced symptoms of anxiety (56%), stress (20%) and depression (4%). As a coping mechanism, a significant relation was observed between spiritual beliefs and anxiety (x (2) = 13.352, p = 0.010), depression (x (2) = 6.205, p = 0.045) and stress (x (2) = 14.833, p = 0.001). Stress among patients increased their likelihood of medication non-adherence [odds ratio (OR) = 2.42 (95% CI 1.06 - 5.5), p = 0.035]. CONCLUSION: The study has demonstrated the need for clinicians to pay attention to negative emotions and their role in medication non-adherence. The recommendation is that attention should be directed toward the use of spirituality as a possible mechanism by which negative emotions could be managed among hypertensive patients.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".