The influence of simulation in predicting intent to stay, among critical care nurses
Bibliographic record
Abstract
Aim: This paper will present a study, which tested a theoretical Critical Care Nurse Retention model and mechanisms that may influence intent to stay in the organization, unit and nursing profession. Background: The current international nursing shortage is worsening and is particularly acute in critical care settings. There is a rapidly aging nursing workforce and at the same time a significant shortfall in the number of new graduates to replace the large numbers of retiring nurses. Intensive care units have been shown to have the highest turnover rates and there is limited scientific evidence on how to retain critical care nurses. One of the most commonly listed incentives for nurses is organizational support in the form of access to educational opportunities and career development. Design: A quasi-experimental longitudinal design was used in a random sample of 363 critical care nurses from multiple hospital sites in Ontario. Method: The 374-hour intervention included an online component, high-fidelity simulation, and a preceptored clinical component. Data Analysis: ANCOVA and hierarchical regression were used to analyze the hypothesized model. Results: Findings showed the professional development intervention had a direct effect on intent to stay in the unit and intent to stay in the profession. Final analysis revealed that the model explained 23% of the variance in intent to stay in the profession. Conclusion: This research provides new evidence supporting the relevance and importance of investing in professional development opportunities and its subsequent impact on intent to stay.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".