Community Pharmacy-Based Blood Pressure Screening in Newfoundland and Labrador, Canada for World Hypertension Day 2022: A Cross-Sectional Study
Bibliographic record
Abstract
Background: High blood pressure (BP) is a leading cause of cardiovascular and stroke-related events. Office-based BP measurement has declined in recent years due to the COVID-19 pandemic, which may have resulted in higher rates of undetected and uncontrolled hypertension. To gain a better idea of adult BP levels in Newfoundland and Labrador, we engaged community pharmacists in BP screening on World Hypertension Day. Methods: Data collection and BP screening occurred on May 17, 2022. Pharmacists and pharmacy students collected 3 seated BP readings from participants, using an automated device. The average of readings 2 and 3 was used to estimate BP, with elevated BP defined as ≥ 140/90 mm Hg, or ≥ 130/80 mm Hg for individuals with diabetes. Data on participant demographics, access to primary care, medical history, and antihypertensive use were also collected. Data analysis included descriptive statistics and logistic regression techniques. Results: A total of 460 participants were included in the analysis. The mean age was 56.3 years (standard deviation: 16.95); 63.3% (n = 291) were female; and 43.7% (n = 201) reported having hypertension. Elevated BP was identified in 27% (n = 123). Of those with elevated BP, 41.5% (n = 51) had no history of diagnosed hypertension. Age, sex, and diabetes were statistically significant predictors of elevated BP in the multivariable model. Conclusions: A large proportion of participants in our study had elevated BP. Targeted measures are needed to improve the detection, treatment, and control of high BP in Newfoundland and Labrador. Community pharmacists can support BP care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".