Policy to practice: Investment in transitioning new graduate nurses to the workplace
Bibliographic record
Abstract
AIM: To analyse nurses' perceptions of the impact of an extended transition programme on key dimensions of care delivery 1-6 years after graduation. The dimensions included decision-making, communication, care management, system integration and commitment. BACKGROUND: Health care employers in Ontario, Canada, can apply for government funding to support an extended transition programme for new graduate nurses that includes orientation and mentorship. METHODS: A cross-sectional study design was used. Nurses who participated in the transition programme were compared with nurses who did not. A survey was administered to a convenience sample of 2369 nurses. RESULTS: There were statistically significant differences between the two groups. Nurses in the transition programme had higher mean scores on the key dimensions of care delivery. Results were confirmed when controlling for length of time since graduation. CONCLUSION: Extended transition benefits new graduate nurses. It has a lasting effect over time and impacts key dimensions of care delivery. It can also enhance workforce integration and reduce turnover. IMPLICATIONS FOR NURSING MANAGEMENT: Responding to the needs of new graduate nurses has potential long-term advantages for health care organisations and can influence both quality and delivery of care.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".