Top Factors in Nurses Ending Health Care Employment Between 2018 and 2021
Bibliographic record
Abstract
Importance: The increase in new registered nurses is expected to outpace retirements, yet health care systems continue to struggle with recruiting and retaining nurses. Objective: To examine the top contributing factors to nurses ending health care employment between 2018 and 2021 in New York and Illinois. Design, Setting, and Participants: This cross-sectional study analyzed survey data (RN4CAST-NY/IL) from registered nurses in New York and Illinois from April 13 to June 22, 2021. Differences in contributing factors to ending health care employment are described by nurses' age, employment status, and prior setting of employment and through exemplar nurse quotes. Main Outcomes and Measures: Nurses were asked to select all that apply from a list of contributing factors for ending health care employment, and the percentage of nurse respondents per contributing factor were reported. Results: A total of 7887 nurses (mean [SD] age, 60.1 [12.9] years; 7372 [93%] female) who recently ended health care employment after a mean (SD) of 30.8 (15.1) years of experience were included in the study. Although planned retirement was the leading factor (3047 [39%]), nurses also cited burnout or emotional exhaustion (2039 [26%]), insufficient staffing (1687 [21%]), and family obligations (1456 [18%]) as other top contributing factors. Among retired nurses, 2022 (41%) ended health care employment for reasons other than planned retirement, including burnout or emotional exhaustion (1099 [22%]) and insufficient staffing (888 [18%]). The age distribution of nurses not employed in health care was similar to that of nurses currently employed in health care, suggesting that a demographically similar, already existing supply of nurses could be attracted back into health care employment. Conclusions and Relevance: In this cross-sectional study, nurses primarily ended health care employment due to systemic features of their employer. Reducing and preventing burnout, improving nurse staffing levels, and supporting nurses' work-life balance (eg, childcare needs, weekday schedules, and shorter shift lengths) are within the scope of employers and may improve nurse retention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".