Nursing Residency Program: A Solution to Introduce New Grads into Critical Care More Safely While Improving Accessibility to Services
Bibliographic record
Abstract
PURPOSE: Critical care units are particularly affected by the shortage of nurses. In order to overcome this problem, many organizations have increased the hiring of newly graduated nurses. This paper describes a residency program developed to facilitate the safe integration of those nurses into critical care and its outcomes. METHODS: A one-year nursing residency program dedicated to nurses with less than a year of experience was implemented in 2008. Recruitment and retention rates, as well as accessibility to critical care, were evaluated. RESULTS: A 46% increase in recruitment rate of newly graduated nurses was observed when comparing the same period of time before and after implementation of the program. Moreover, the one-year retention rate rose by 26%; the retention rate, without considering the time since the beginning of employment, rose by 71%. As for accessibility to critical care, it increased by 50% (from 24 to 36 beds). Finally, the program was favourably evaluated by experienced nurses in terms of skills and critical thinking development among nursing residents. CONCLUSION: A nursing residency program developed to meet the needs of inexperienced nurses and integrate them into high-acuity settings appears to be one solution to resolving undesirable limited access to safe-quality critical 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".