Insights from the 2023 May measurement month campaign in Newfoundland and Labrador, Canada: A cross-sectional study
Bibliographic record
Abstract
May measurement month (MMM) is a global blood pressure (BP) screening campaign that aims to emphasize the importance of BP measurement and identify those who require intervention/follow-up for elevated BP. The objective of this regional analysis in Newfoundland and Labrador (NL), Canada, was to examine the proportion of individuals screened with elevated BP, including those with and without a history of hypertension (HTN). This cross-sectional study was completed in accordance with the global MMM protocol. All consenting adults ≥18 years old were eligible to take part. Data collection took place in 28 community pharmacies across the province of NL. Descriptive statistics were analyzed and associations between elevated BP and covariates of interest were determined using logistic regression. A total of 384 participants took part in this study, with a mean age of 54.4 years (standard deviation 18.2); 66.1% (n = 254) of participants were female and 41.4% (n = 159) had known HTN. A complete set of 3 BP readings were recorded for a total of 375 participants and therefore, these participants were included in the analysis. Elevated BP was observed in 21.9% (n = 82) of participants, including 13.5% of those who had no history of HTN (i.e., 30 of 222). Known HTN and diabetes were statistically significant predictors of elevated BP in the multivariate regression model. Regional implementation of the MMM campaign in NL helped to identify a relatively large proportion of individuals with elevated BP, including those with no history of HTN. Targeted measures are needed to achieve BP targets among individuals with hypertension and diabetes in the province.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".