The long and winding road: Integration of nurse practitioners and clinical nurse specialists into the Canadian health-care system.
Bibliographic record
Abstract
We are honoured to be co-guest editors of this issue of CJNR focused on advanced practice nursing (APN). In Canada, advanced practice nurses include nurse practitioners (NPs) and clinical nurse specialists (CNSs) (Canadian Nurses Association [CNA], 2008). It is fitting that CJNR is publishing this APN-focused issue given the leadership that Moyra Allen, founding editor of the Journal, demonstrated in her early writings about “the expanded role in nursing” (Allen, 1977). The research pieces and feature articles in this issue reflect the growing contribution of APN roles to the health of Canadians and highlight areas where further work is required to maximize their integration into the health-care system. NPs are “registered nurses with additional educational preparation and experience who possess and demonstrate the competencies to autonomously diagnose, order and interpret diagnostic tests, prescribe pharmaceuticals and perform specific procedures within their legislated scope of practice” (CNA, 2009b, p. 1). Those who are registered as family/all-ages or primary health care NPs typically work in the community, in settings such as community health centres, family physician offices, and long-term-care facilities, with a focus on health promotion, preventive care, diagnosis and treatment of acute common illnesses and injuries, and monitoring and management of stable chronic diseases. Those who are registered as adult, pediatrics, or neonatal NPs (also known as acute-care NPs) typically provide advanced nursing care across the continuum of acute-care services for patients who are acutely, critically, or chronically ill with complex conditions. They work in areas such as oncology, neonatology, and cardiology. In 2008, there were 1,626 licensed NPs in Canada (Canadian Institute for Health Information [CIHI], 2010). CJNR 2010, Vol. 42 No 2, 3–8
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".