Effective patient safety education for novice RNs: A systematic review
Bibliographic record
Abstract
Background and objective: The need is great for identifying effective evidence-based strategies that focus on increasing novice RN confidence for the application of skills used to care for patients safely. The purpose of this systematic review is to explore effective continuing education strategies that target novice RNs’ professional development, enhance clinical confidence, and focus on patient safety.Methods: The EBSCOhost database search was set to find recently published papers within the last ten years, sorted by relevance from January 2007 through August 2017. This search yielded twelve studies deemed eligible for inclusion by the databases CINAHL, Communication & Mass Media Complete, Education Full Text (H.W. Wilson), Health & Wellness Resource Center, and Science Direct. Commonalities and distinguishing features among the strategies are examined.Results: This systematic review identified 12 articles that describe effective training strategies aimed at improving novice RNs’ clinical practice confidence and skill. A thematic analysis of the data was used to systematically gain knowledge about strategies used to educate novice RNs working in the hospital setting. The majority of strategies employed a number of different types of simulation and reported varying degrees of success for improving novice RN ability to care for patients safely. Simulation, virtual reality, preceptored clinical experiences, and interdisciplinary experiences were found to be effective education strategies enhancing novice RN’s skill for providing safe care. Didactic instruction had positive results, but was not as effective as simulation for novice RNs learning safe patient care. Finally, written instruction was not as effective as simulation, and hard copy supplements provided no added value to novice RNs learning safe patient care.Conclusions: Findings from this review are foundational to address calls from the Institute of Medicine (IOM) and the National League for Nursing (NLN) to reform and support post-graduate nursing education. The development of novel education and training targeting novice RNs in the hospital setting is essential, but more research is needed to enhance safe patient care.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".