Hypertension, knowledge, attitudes, and practices of primary care physicians in Ulaanbaatar, Mongolia
Bibliographic record
Abstract
We examined the knowledge, attitudes, and practices of primary care doctors in Ulaanbaatar, Mongolia using a recently developed World Hypertension League survey. The survey was administered as part of a quality assurance initiative to enhance hypertension control. A total of 577 surveys were distributed and 467 were completed (81% response rate). The respondents had an average age of 35 years and 90.1% were female. Knowledge of hypertension epidemiology was low (13.5% of questions answered correctly); 31% of clinical practice questions had correct answers and confidence in performing specific tasks to improve hypertension control had 63.2% "desirable/correct" answers. Primary care doctors mostly had a positive attitude toward hypertension management (76.5% desirable/correct answers) and highly prioritized hypertension management activities (85.7% desirable/correct answers). Some important highlights included the majority (> 80%) overestimating hypertension awareness, treatment, and control rates; 78.2% used aneroid blood pressure manometers; 15% systematically screened adults for hypertension in their clinics; 21.8% reported 2 or more drugs were required to control hypertension in most people; and 16.1% reported most people could be controlled by lifestyle changes alone. 55% of respondents were not comfortable prescribing more than 1 or 2 antihypertensive drugs in a patient and the percentage of desirable/correct responses to treating various high-risk patients was low. Most (53%-74%) supported task shifting to nonphysician health care providers except for drug prescribing, which only 13.9% supported. A hypertension clinical education program is currently being designed based on the specific needs identified in the survey.
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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 | 0.001 | 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".