The Implications of Nurse Mentorship on New Graduate Nurses’ Outcomes
Bibliographic record
Abstract
The COVID-19 pandemic has magnified the growing issue of nurse turnover, especially among new graduate nurses (NGNs). The purpose of this study was to describe, compare, and examine the mentorship relationships of NGNs in Ontario across multiple healthcare settings, and to investigate the relationships between mentorship quality (MQ), occupational coping self-efficacy (OCSE), and turnover intention (TI). A descriptive cross-sectional design was utilized with a sample of 128 NGNs employed in long-term care, community care, and hospital settings The COVID-19 pandemic has magnified the growing issue of nurse turnover, especially among new graduate nurses (NGNs). The purpose of this study was to describe, compare, and examine the mentorship relationships of NGNs in Ontario across multiple healthcare settings, and to investigate the relationships between mentorship quality (MQ), occupational coping self-efficacy (OCSE), and turnover intention (TI). A descriptive cross-sectional design was utilized with a sample of 128 NGNs employed in long-term care, community care, and hospital settings. Data collected using an online survey were analyzed using SPSS version 27. The PROCESS macro was used to test a hypothesized mediation relationship between MQ and TI through OCSE. MQ was found to be the most statistically significant predictor of both OCSE and TI, with no significant mediation effect found. Managers, educators, and policymakers should develop strategies to optimize NGNs’ access to high-quality mentorship experiences, to mitigate negative outcomes associated with NGN turnover.. Data collected using an online survey were analyzed using SPSS version 27. The PROCESS macro was used to test a hypothesized mediation relationship between MQ and TI through OCSE. MQ was found to be the most statistically significant predictor of both OCSE and TI, with no significant mediation effect found. Managers, educators, and policymakers should develop strategies to optimize NGNs’ access to high-quality mentorship experiences, to mitigate negative outcomes associated with NGN turnover.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".