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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".