Additive association of knowledge and awareness on control of hypertension: a cross-sectional survey in rural India
Bibliographic record
Abstract
OBJECTIVE: To determine whether there is an interaction between knowledge about hypertension and awareness of hypertension on the treatment and control of hypertension in three regions of South India at different stages of epidemiological transition (see Video, Supplemental Digital Content 1, http://links.lww.com/HJH/B426). METHODS: Using a cross-sectional design, we randomly selected villages within each of rural Trivandrum, West Godavari, and Chittoor. Sampling was stratified by age group and sex. We measured blood pressure and administered a questionnaire to determine knowledge and awareness of hypertension. Logistic regression was used to assess associations of awareness and knowledge about hypertension with its treatment and control in participants with hypertension, while examining for statistical interaction. RESULTS: Among a total of 11 657 participants (50% male; median age 45 years), 3455 had hypertension. In analyses adjusted for age and sex, both knowledge score [adjusted odds ratio (aOR) 1.14 [95% confidence interval (CI) 1.12--1.17)] and awareness [aOR 104 (95% CI 82--134)] were associated with treatment for hypertension. Similarly, both knowledge score [aOR 1.10; 95% CI (1.08--1.12)] and awareness [aOR 13.4; 95% CI (10.7--16.7)], were positively associated with control of blood pressure in those with hypertension, independent of age and sex. There was an interaction between knowledge and awareness on both treatment and control of hypertension (P of attributable proportion <0.001 for each). CONCLUSION: Health education to improve knowledge about hypertension and screening programs to improve awareness of hypertension may act in an additive fashion to improve management of hypertension in rural Indian populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".