Systematic Review: Bridging the Gap in RPN‐to‐RN Transitions
Bibliographic record
Abstract
PURPOSE: To review the evidence examining the influences of successful education and professional role transition for registered practical nurses (RPNs) pursuing a baccalaureate degree in nursing (BScN) and registered nurse (RN) licensure through RPN-to-RN bridging programs. DESIGN: Systematic review of papers published between 1995 and 2014 that evaluated students' education and professional role transitions from RPN to RN. METHODS: Thirty-nine papers were selected that observed or studied the change or transition in designation from RPN to RN, or its equivalent, through bridging programs and analyzed thematically according to Meleis, Sawyer, Im, Hilfinger Messias, and Schumacher's transition model. FINDINGS: Personal, community, and social conditions related to preparation for entry, program enrolment, and postgraduate clinical integration influence successful education and professional role transitions for RPN-to-RN bridging students. CONCLUSIONS: Providing key transition supports may enhance the potential for successful student transition into and throughout a bridging program, but further research is necessary to enhance this understanding and to recommend best practices for optimizing students' success. CLINICAL RELEVANCE: The evidence from this review identifies facilitators and barriers to successful education and professional role transition for RPN-to-RN bridging students, and identifies important considerations for future research.
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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 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".