Increasing Retention of New Graduate Nurses
Bibliographic record
Abstract
With the nursing shortage and the high incidence of turnover among new graduate nurses (NGNs) within the first year of employment, there is an increased need to investigate the effectiveness of retention strategies aimed at retraining NGNs. The purpose of this articled was to determine which organizational strategies increase the retention rates of NGNs. A systematic review of the research literature was conducted to examine published studies that focused on a retention strategy implemented to influence NGNs to stay in their place of employment. Data were extracted, and the quality of each study was assessed. Sixteen published studies were included in this review. Of these, 13 did not use true experimental study designs. Based on the studies with the strongest designs, the highest retention rates were associated with retention strategies that used a preceptor program model that focused on the NGN as well as a program length of 3 to 6 months. Evidence for the effectiveness of implementation strategies is limited; however, it is apparent from all the studies reviewed that implementing a retention strategy is effective for increasing retention rates of NGNs.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".