Post-graduate Integration Programs for Recently Graduated Nurse Practitioners: A Rapid Review
Bibliographic record
Abstract
Aim: The purpose of this paper is to present a rapid review of the literature that describes and evaluates post-graduate professional integration programs for recently graduated nurse practitioners. Background: Recently graduated nurse practitioners face numerous challenges upon entry to practice, these include high patient caseloads, lack of confidence, and difficulty integrating into the interprofessional team. In response to these challenges, numerous post-graduate professional integration programs have emerged to support the transition of recently graduated nurse practitioners into practice. Design: A rapid review was conducted following McMaster University Rapid Review Guidebook’s Guidelines. Method: Studies describing and evaluating post-graduate professional integration programs for nurse practitioners who recently graduated from master’s or doctoral programs were included. The Template for Intervention Description and Replication (TIDieR) guidelines were used to assess the quality of the description of the programs in the included studies. A narrative synthesis was completed. Results: Among 2,261 records yielded from the electronic search, 27 studies on 26 post-graduate professional integration programs were included in this review. The quality of the description of the programs included was high. Three types of programs were identified in this review: residency programs, fellowship programs, and transition-to-practice programs. Studies suggested that post-graduate professional integration programs increase the confidence level, degree of satisfaction, skill acquisition, knowledge, and productivity of recently graduated nurse practitioners. Conclusions: Post-graduate professional integration programs represent promising avenues to support the transition of recently graduated nurse practitioners into practice. Future research is needed to better understand the relative benefits and effectiveness of different post-graduate professional integration program structures across specific outcomes.
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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 | 0.028 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| 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".