Worksite-based cardiovascular risk screening and management: a feasibility study
Bibliographic record
Abstract
Background: Established cardiovascular risk factors are highly prevalent and contribute substantially to cardiovascular morbidity and mortality because they remain uncontrolled in many Canadians. Worksite-based cardiovascular risk factor screening and management represent a largely untapped strategy for optimizing risk factor control. Methods: In a 2-phase collaborative demonstration project between Alberta Health Services (AHS) and the Alberta Newsprint Company (ANC), ANC employees were offered cardiovascular risk factor screening and management. Screening was performed at the worksite by AHS nurses, who collected baseline history, performed automated blood pressure measurement and point-of-care testing for lipids and A1c, and calculated 10-year Framingham risk. Employees with a Framingham risk score of ≥10% and uncontrolled blood pressure, dyslipidemia, or smoking were offered 6 months of pharmacist case management to optimize their risk factor control. Results: In total, 87 of 190 (46%) employees volunteered to undergo cardiovascular risk factor screening. Mean age was 44.5±11.9 years, 73 (83.9%) were male, 14 (16.1%) had hypertension, 4 (4.6%) had diabetes, 12 (13.8%) were current smokers, and 9 (10%) had dyslipidemia. Of 36 employees with an estimated Framingham risk score of ≥10%, 21 (58%) agreed to receive case management and 15 (42%) attended baseline and 6-month follow-up case management visits. Statistically significant reductions in left arm systolic blood pressure (−8.0±12.4 mmHg; p =0.03) and triglyceride levels (−0.8±1.4 mmol/L; p =0.04) occurred following case management. Conclusion: These findings demonstrate the feasibility and usefulness of collaborative, worksite-based cardiovascular risk factor screening and management. Expansion of this type of partnership in a cost-effective manner is warranted. Keywords: blood pressure, dyslipidemia, smoking, pharmacist, worksite
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".