Adherence to treatment among hypertensive individuals in a rural population of North India
Bibliographic record
Abstract
Background: Hypertension affects nearly a quarter of adults in India. While there are issues related to diagnosis and treatment gap, even among those who received treatment, adherence is a problem resulting in poor control. Aim & Objective: To study the adherence to treatment of hypertension and its determinants among rural population Methods and Material: A community based cross-sectional study was carried out in twenty-eight villages in Ballabgarh block of Faridabad district of Haryana. Sample size of 300 was calculated. Adults (? 18 years) with self-reported hypertension were recruited by simple random sampling at community level. Adherence to treatment was studied by both recall and pill count methods. Information about socio-demographic characteristic was also obtained. Results: In total 350 participants were recruited in the study. Adherence (100%) by recall method was reported among 27.4% subjects and by pill count among 18.9% subjects. Symptom-free period was identified as most common reason for non-adherence. Statistically significant poor adherence to treatment of hypertension was reported among subjects belonging lower social strata. Conclusions: Very low adherence to hypertension treatment was reported in rural community in northern India. There is urgent need for awareness generation about treatment adherence and developing adherence-monitoring mechanisms at community level
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".