Ups and downs of hypertension control in Canada: critical factors and lessons learned
Bibliographic record
Abstract
As the leading risk for death, population control of increased blood pressure represents a major challenge for all countries of the Americas. In the early 1990's, Canada had a hypertension control rate of 13%. The control rate increased to 68% in 2010, accompanied by a sharp decline in cardiovascular disease. The unprecedented improvement in hypertension control started around the year 2000 when a comprehensive program to implement annually updated hypertension treatment recommendations started. The program included a comprehensive monitoring system for hypertension control. After 2011, there was a marked decrease in emphasis on implementation and evaluation and the hypertension control rate declined, driven by a reduction in control in women from 69% to 49%. A coalition of health and scientific organizations formed in 2011 with a priority to develop advocacy positions for dietary policies to prevent and control hypertension. By 2015, the positions were adopted by most federal political parties, but implementation has been slow. This manuscript reviews key success factors and learnings. Some key success factors included having broad representation on the program steering committee, multidisciplinary engagement with substantive primary care involvement, unbiased up to date credible recommendations, development and active adaptation of education resources based on field experience, extensive implementation of primary care resources, annual review of the program and hypertension indicators and developing and emphasizing the few interventions important for hypertension control. Learnings included the need for having strong national and provincial government engagement and support, and retaining primary care organizations and clinicians in the implementation and evaluation.
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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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".