Examining the Potential of Communities of Practice to Facilitate New Graduate Nurse Practitioner Transition to Practice
Bibliographic record
Abstract
Nurse Practitioners (NPs) are described by the Canadian Nurses Association (CNA) (2016) as unique health care professionals who have a combination of knowledge of nursing theory and medical skills. The turnover rate for NPs is double that of physicians. This trend of high turnover has been described as frequently relating to NPs’ transition to practice, specifically as new graduates. There are several facilitators to transition to practice for NPs found in the literature. There is some uptake by organizations to utilize these facilitators; however, many organizations do not use any. It is unknown why or why not organizations are using the facilitators when the evidence shows positive outcomes. The purpose of this pre-implementation study was to examine the perceived fit of using a community of practice (COP) to facilitate new graduate NP transition to practice in Nova Scotia (NS), Canada. This study used a qualitative interpretive description approach to address the study aims through individual interviews with 14 participants from different groupings that would affect implementation of COPs within a healthcare organization. Qualitative data wasanalyzed using inductive thematic analysis. This data was then deductively analyzed using the consolidated framework for implementation science (CFIR) to identify the barriers and facilitators to implementation. Barriers to implementation were found to be time, money, lack of senior NPs, and a lack of decision maker support and understanding. The facilitators of this intervention included a COP’s potential to foster a sustainable workforce, facilitate knowledge sharing through peer support, and be cost saving by using one NP to transition multiple new graduates. This study resulted in improved understanding of the barriers and facilitators to implementing COPs for new graduate NP transition within healthcare organizations. Insights from this study can be used to guide future implementation of COPs as a transition facilitator to improve the retention of new graduate NPs.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".