Analyzing U.S. nurse turnover: Are nurses leaving their jobs or the profession itself?
Bibliographic record
Abstract
Objective: To examine and compare factors associated with making the decision to vacate a job (organizational turnover) versus leaving the profession (professional turnover) among registered nurses (RN) in the United States (U.S.).Methods: Nationally representative data from the 2008 National Sample Survey of Registered Nurses was used. The sample consisted of 8,796 RNs who held an active RN license as of March 10, 2008, but changed a place of work or left the profession entirely. The analysis has been performed using SAS, version 9.3.Results: The results of binary logistic regression revealed that RNs who reported work-related disability (OR = 14.51; p-value: < .001), illness (OR = 3.32; p-value: < .001), experienced high physical demands (OR = 1.57; p-value: < .001) or burnout (OR = 1.39; p-value: < .001), were unsatisfied with their schedule (OR = 2.16; p-value: < .001), or staffing arrangements (OR = 1.41; p-value: < .001) were more likely to leave the profession. Whereas RNs who reported high levels of stress (OR = 0.59; p-value: < .001) were unsatisfied with the organization’s leadership (OR = 0.22; p-value: < .001), unsatisfied with their opportunity to advance their career (OR = 0.56; p-value: < .001), or were not adequately compensated (OR = 0.63; p-value: < .001), were more likely to leave the organization.Conclusions: Policy makers and health care managers should be aware of the different factors that are associated with RNs’ decision to leave the profession or an organization. Health care managers involved in the development of nurse retention strategies should address organizational leadership and consider development of comprehensive career development programs. Policy makers should consider allocating additional resources to ensure that RN workforce is of adequate size, is qualified, and is able to provide high quality care in the U.S..
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".