Patients’ Knowledge, Attitudes, Behaviour and Health Care Experiences on the Prevention, Detection, Management and Control of Hypertension in Colombia: A Qualitative Study
Bibliographic record
Abstract
Hypertension is a leading cause of premature death worldwide and the most important modifiable risk factor for cardiovascular disease. Effective screening programs, communication with patients, regular monitoring, and adherence to treatment are essential to successful management but may be challenging in health systems facing resource constraints. This qualitative study explored patients' knowledge, attitudes, behaviour and health care seeking experiences in relation to detection, treatment and control of hypertension in Colombia. We conducted in-depth interviews and focus group discussions with 26 individuals with hypertension and 4 family members in two regions. Few participants were aware of ways to prevent high blood pressure. Once diagnosed, most reported taking medication but had little information about their condition and had a poor understanding of their treatment regime. The desire for good communication and a trusting relationship with the doctor emerged as key themes in promoting adherence to medication and regular attendance at medical appointments. Barriers to accessing treatment included co-payments for medication; costs of transport to health care facilities; unavailability of drugs; and poor access to specialist care. Some patients overcame these barriers with support from social networks, family members and neighbours. However, those who lacked such support, experienced loneliness and struggled to access health care services. The health insurance scheme was frequently described as administratively confusing and those accessing the state subsidized system believed that the treatment was inferior to that provided under the compulsory contributory system. Measures that should be addressed to improve hypertension management in Colombia include better communication between health care professionals and patients, measures to improve understanding of the importance of adherence to treatment, reduction of co-payments and transport costs, and easier access to care, especially in rural areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".