Retention of RN-to-BSN Students: An Integrative Review
Bibliographic record
Abstract
Background Student retention is problematic in higher education. RNs who desire to complete a Bachelor of Science in Nursing (RN-to-BSN) program face multiple barriers, and undergraduate nursing programs must be creative and comprehensive in their efforts to retain students and support their progression. Method Toronto and Remmington's method guided this integrative review to identify strategies undergraduate nursing programs employ to retain RN-to-BSN students. A systematic search of seven databases was conducted to identify literature published in the United States between 2011 and 2024. Results Twenty articles were analyzed and synthesized, then organized using Jeffrey's nursing universal retention and success model. Most of the literature reviewed focused on environmental and professional integration factors, with less of the literature addressing student affective factors. Conclusion Although programs may differ, a framework that considers a variety of intrinsic and extrinsic factors is imperative in addressing issues specific to RN-to-BSN student retention. [ J Nurs Educ . 2025;64(7):429–435.]
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".