Post-licensure nurse training in U.S. health systems: Survey insights into modalities, effectiveness, and perceived gaps
Bibliographic record
Abstract
Background and objective: As the practice of nursing requires broad clinical skills, it demands a wide range of understanding across many disciplines. To address nurse training and competency needs, health systems have implemented varying methods of skill development, including advanced technologies and virtual immersion strategies, in addition to the continuation of more conventional methods. The current literature remains limited in substantiating the effectiveness of a particular method. The objective of the study was to provide insights into the current post-licensure nurse training practice within U.S. health systems, nurse perceptions of training effectiveness for the given methods, and perceived gaps in nurse education. Methods: Design: A research survey of registered nurses across the United States. The survey was developed from published studies that describe the current training modalities deployed across U.S. health systems for nurses and based on direct feedback from practicing nurses. The survey was divided into the following sections: respondent demographics, commonly deployed training strategies, perceived effectiveness of different training methods, and perceived gaps in clinical practice training and competence. Results: A total of 247 nurse survey responses were included in the analysis. The results suggest that there is a disconnect between commonly deployed post-licensure nurse training strategies and the strategies that are perceived to be most valuable. There were several perceived gaps in core clinical nursing skills, highlighting an opportunity to improve upon current training strategies. Conclusions: This study provides insights into the current state of post-licensure nurse training and signals on where health systems may benefit from reassessing their educational strategies and where industry organizations may provide additional support by developing effective multi-modal education.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".