Primary prevention of cardiovascular disease on Prince Edward Island: An NP-led initiative
Bibliographic record
Abstract
In Prince Edward Island (PEI), the ever-rising prevalence of chronic diseases such as\ncardiovascular disease (CVD) represents the most significant strain on healthcare resources\n(Health PEI, 2013a). Cardiovascular disease is considered to be preventable, particularly when\nrisk factors such as diabetes, hypertension, and dyslipidemia are detected and treated early\n(Public Health Agency of Canada [PHAC], 2016a). When risk factors are left untreated, and\nCVD develops, critical complications such as heart failure, acute coronary syndrome, stroke, and\ndeath can occur (PHAC, 2016b). Factors that contribute to the lack of appropriate, evidence-based\nCVD prevention in PEI include a lack of access to primary care providers, a lack of\nstrategic health policy for the prevention and management of CVD, and poor adherence to\nclinical practice guidelines among primary care providers across Canada (Canadian Medical\nAssociation [CMA], 2011; Kreatsoulas & Anand, 2010; Mosca et al., 2005). A recent shift\ntowards primary prevention of CVD has begun with promising approaches for more effective\nprimary prevention seen on an international, national, and provincial scale. A nurse practitioner-led\nCVD primary prevention program can improve access to high-quality, evidence-based care to\nthose adults at risk of CVD through care plans aimed at risk factor stratification, health\npromotion, and illness prevention. The proposed program will fill a gap in existing healthcare\nprogramming and is designed to decrease rates of morbidity and mortality related to CVD in PEI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".