Development of a Nurse Practitioner Fellowship Program in Cardiometabolic Health
Bibliographic record
Abstract
In a landscape marked by the increasing prevalence of cardiovascular-kidney-metabolic (CKM) syndrome, there is a pressing need for novel health care models that offer optimal care to affected individuals. The current care system, marked by fragmentation and a lack of holistic approaches, falls short in addressing the multi-faceted needs of patients living with CKM syndrome, highlighting the necessity for a tailored and comprehensive care model. Nurse practitioners (NPs) are uniquely positioned to pioneer these innovative models, leveraging their nursing skills and incorporating medical activities into their practice. However, specialized education is essential, emphasizing a deep understanding of the complex, chronic, and progressively worsening nature of CKM syndrome. Fellowship programs are pivotal in equipping NPs with the expertise required to enact this advanced cardiometabolic health care paradigm. This article presents the process of development of a new NP fellowship program in cardiometabolic health developed by the faculty of nursing at a francophone university. This narrative outlines the context surrounding the development process of this program, stemming from the escalating incidence of cardiometabolic conditions compounded by the inadequacies of existing health care models. Addressing this gap, the proposed fellowship program aimed to refine NPs’ capabilities in managing cardiometabolic health intricacies through an immersive, multidisciplinary educational experience. This initiative represents the first of its kind in North America to be conducted in French and is designed to elevate the standard of care for individuals living with CKM syndrome. This paper encompasses a literature review and the adoption of conceptual frameworks like Calkin’s (1984) model for advanced nursing practice. The program’s structure, pedagogical strategies, and expected outcomes are thoroughly examined, showcasing the comprehensive approach taken to equip NPs with the skills and knowledge necessary for this specialized domain. The article argues for the critical role of specialized preparation in advancing NPs’ competencies, ultimately contributing to improved patient outcomes in cardiometabolic health. By fostering a deeper understanding and more effective management strategies, the fellowship program is poised to set a new standard in health care delivery for this growing patient population.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".