Advanced Practice Nursing in Canada: Overview of a Decision Support Synthesis
Bibliographic record
Abstract
The objective of this decision support synthesis was to identify and review published and grey literature and to conduct stakeholder interviews to (1) describe the distinguishing characteristics of clinical nurse specialist (CNS) and nurse practitioner (NP) role definitions and competencies relevant to Canadian contexts, (2) identify the key barriers and facilitators for the effective development and utilization of CNS and NP roles and (3) inform the development of evidence-based recommendations for the individual, organizational and system supports required to better integrate CNS and NP roles into the Canadian healthcare system and advance the delivery of nursing and patient care services in Canada. Four types of advanced practice nurses (APNs) were the focus: CNSs, primary healthcare nurse practitioners (PHCNPs), acute care nurse practitioners (ACNPs) and a blended CNS/NP role. We worked with a multidisciplinary, multijurisdictional advisory board that helped identify documents and key informant interviewees, develop interview questions and formulate implications from our findings. We included 468 published and unpublished English- and French-language papers in a scoping review of the literature. We conducted interviews in English and French with 62 Canadian and international key informants (APNs, healthcare administrators, policy makers, nursing regulators, educators, physicians and other team members). We conducted four focus groups with a total of 19 APNs, educators, administrators and policy makers. A multidisciplinary roundtable convened by the Canadian Health Services Research Foundation formulated evidence-informed policy and practice recommendations based on the synthesis findings. This paper forms the foundation for this special issue, which contains 10 papers summarizing different dimensions of our synthesis. Here, we summarize the synthesis methods and the recommendations formulated at the roundtable.
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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 | 0.191 | 0.298 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.085 | 0.111 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.032 | 0.007 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".