Abstract PD-032: NEW GRADUATE NURSES IN PEDIATRIC CRITICAL CARE: WHERE ARE THEY NOW?
Bibliographic record
Abstract
Aims & Objectives: Direct recruitment of new graduate nurses (NGN) to critical care (CC) can address shortfalls in nurse staffing. The one and three-year outcomes of our learner-centered program for NGN integration to CC have been previously presented (2011). We describe the 10-year outcomes of the graduates of this education program. Methods A mixed method evaluation of program outcomes was performed using survey, semi-structured interview and administrative record review. Main outcomes were retention, career achievement and NGN satisfaction. Eligible participants were practicing nurses who had participated in the NGN program and 3-year evaluation. Three and ten year results were compared. Descriptive statistical and thematic results are presented. Results Of the nurses eligible for the 10-year survey 57% (28/49) participated. Hospital retention was 83% (43/52) at 3-years, and 57% (27/47) at 10-years. Retention in ICU was 71% (37/52) at 3-years, and 35% (18/52) at 10-years. 67% (12/18) of those still practicing in CC intend to remain. Participants identified reasons for leaving as; life choices, ongoing education and new roles in the organization including leadership positions such as advanced practice, senior manager and educator. 82% (23/28) were satisfied with their NGN experience and would choose this experience again. Conclusions Retention in NGN was similar to non-NGN hires. NGN direct recruitment to CC is a viable staffing strategy with high NGN satisfaction over the long term. An integrated learner-centred orientation program with graduated clinical supervision was positively impactful on NGN professional development in the graduate, early and mid-career stages.
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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 | 0.004 | 0.009 |
| 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 | 0.001 | 0.001 |
| 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".