Identifying the key predictors for retention in critical care nurses
Bibliographic record
Abstract
AIMS: The aim of this study was to explore the key predictors of retention in nurses working in critical care areas. BACKGROUND: The shortage of critical care nurses is reaching crisis proportions in Canada and throughout the industrialized world. Identifying the key influencing (i.e. person and organizational) factors and intermediary factors (i.e. job satisfaction, engagement, professional quality of life and caring) that affect intent to leave is central to developing optimal retention strategies for critical care nurses. DESIGN: As part of a larger mixed-methods study, we used a quantitative, cross-sectional research design. A novel framework: the Conceptual Framework for Predicting Nurse Retention was used to guide this study. METHODS: On-line survey data were collected from on a convenience sample of 188 registered nurses working in critical care areas of hospitals in the province of Manitoba, CANADA in 2011. RESULTS: Twenty-four per cent of the respondents reported that they would probably/definitely leave critical care in the next year. Based on bivariate and regression analyses, the key influencing factors that were significantly related to the intermediary factors and intent to leave critical care and nursing included: professional practice, management, physician/nurse collaboration, nurse competence, control/responsibility and autonomy. Of the intermediary factors, all but compassion satisfaction were related to intent to leave both critical care and nursing. CONCLUSION: This study highlights the importance of exploring multiple organizational and intermediary factors to determine strategies to retain critical care nurses. The findings also support the Conceptual Framework for Predicting Nurse Retention as a theoretical basis for further research.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".