Analyzing U.S. nurse turnover: Are nurses leaving their jobs or the profession itself?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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..
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it