Evaluating New Graduate Nurse Readiness for Practice
Bibliographic record
Abstract
BACKGROUND: New graduate nurses (NGNs) often feel unprepared to enter the nursing workforce. Nurse educators can collaborate with clinical partners to identify gaps in practice readiness to meet the current needs of novice nurses and improve their preparation for practice. PURPOSE: To evaluate perceived practice readiness among NGNs on hire and to identify areas for potential improvement in the nursing curriculum. METHODS: A retrospective cross-sectional study was conducted on NGN readiness using Casey-Fink Graduate Nurse Experience Surveys. Data from 273 NGNs hired between 2021 and 2023 within 1 large nonprofit academic hospital were analyzed. RESULTS: Over 75% of NGNs reported feeling comfortable/confident with communication, delegation, and organizing/prioritizing patient care needs. The primary challenges highlighted included lack of confidence and providing end-of-life care. NGNs often feel uncomfortable independently managing codes, ventilators, and chest tubes. CONCLUSION: Nurse educators should prioritize clinical experiences to increase confidence and reinforce training for providing end-of-life care and complex hands-on skills.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".