Factors affecting nurse practitioner role implementation in Canadian practice settings: an integrative review
Bibliographic record
Abstract
AIM: To review the literature about the Canadian experience with nurse practitioner role implementation and identify influencing factors. BACKGROUND: Although nurse practitioners have been in existence for more than 40 years, their integration into healthcare systems has been challenging. While frameworks exist to guide implementation of these roles, clear identification of factors influencing role implementation may inform best practices. Given that Canada has witnessed considerable growth in nurse practitioner positions in the past decade, an exploration of its experience with role implementation is timely. DATA SOURCES: A review of Canadian literature from 1997 to 2010 was conducted. Electronic databases including CINAHL, Cochrane Database of Systematic Reviews, Health Source: Nursing Academic Edition, Medline, Social Science Index, PubMed, Web of Science and PsychINFO and government and professional organization websites were searched. METHODS: An integrative review was performed guided by Whittemore and Knafl's method. RESULTS: Ten published studies and two provincial reports were included. Numerous facilitators and barriers to implementation were identified and analysed for themes. Three concepts influencing implementation emerged: involvement, acceptance and intention. Involvement is defined as stakeholders actively participating in the early stages of implementation. Acceptance is recognition and willingness to work with nurse practitioner. Intention relates to how the role is defined. CONCLUSION: This integrative review revealed three factors that influence nurse practitioner role implementation in Canada: involvement, acceptance and intention. Strategies to enhance these factors may inform best practice role implementation processes.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".