Interventions Supporting New Graduate Nurse Transition into Critical Care: A Systematic Review
Bibliographic record
Abstract
Background: New graduate nurses have reported feelings of disorientation, discouragement, overwhelm and lack of confidence as they transition from nursing school to the practice environment. These feelings and a poor transition to practice can lead to burnout and turnover. New graduate nurses generally have a high turnover risk. Given the demands of specialty nursing practice environments like the intensive care unit, new graduates transitioning to critical care may be at an even higher risk of a poor transition to practice and risk of turnover. Aims: To present preliminary findings from a systematic review that aimed to identify, describe and report on the effectiveness of interventions that support new graduate nurse transition into critical care settings. Methods: A systematic review modelled on Joanna Briggs Institute methodology was designed. A systematic, peer-reviewed search was conducted in the following databases: OvidMEDLINE ALL, Embase Classic+ Embase, PsychINFO on OVID, CINAHL and Education Source on Ebsco, Nursing and Allied Health and ERIC databases, in addition to a grey literature search. For inclusion, the population of interest was new graduate nurses and all forms of intervention about transitioning new graduate nurses into critical care clinical practice were considered. Contexts of care encompassed all critical care settings as well as emergency departments. Findings: At this stage of the review, twenty-six articles met inclusion criteria encompassing interventions implemented in four countries: USA, Canada, China and Australia. Findings related to intensive care units as well as emergency departments were reported. All interventions were unique in nature resulting in a significantly heterogeneous sample. Where qualitative designs were used, four synthesized themes were generated. Conclusions: Preliminary review findings suggest that, to date, interventions implemented to support new graduate nurse transition into intensive care unit and emergency department contexts are unique and thus heterogeneous in nature. Despite the heterogeneity, common intervention elements are discernable and provide some direction regarding practices that support new graduate transition into 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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".