Changes in Registered Nurse Employment and Education Capacity during the COVID‐19 Pandemic and the Risk of Future Shortages
Bibliographic record
Abstract
Research Objective During the first months of the COVID‐19 pandemic, numerous concerns about the nursing workforce were reported. Nursing education programs have reported that their students were not able to continue their clinical education due to worries about infection risks within hospitals and some have cancelled or reduced entering cohorts. At the same time, anecdotal reports suggested that some RNs near retirement chose to retire early to reduce the risk of infection with SARS‐Cov2. These changes, if true, could undermine the progress made over the past 20 years toward a balanced nursing labor market and lead to shortages of RNs in the near future. Study Design This study uses data from two surveys conducted in California to assess the current and future supply of RNs, and to learn how the coronavirus pandemic is affecting this essential workforce. Early data from two surveys have been analyzed to provide a rapid assessment of the workforce: (1) the biennial Survey of California Registered Nurses, and (2) the Board of Registered Nursing Annual Schools Survey. Data from the Survey of California RNs, which is based on a stratified sample of the state's nurses, are weighted to represent the total population of nurses. Analysis methods include tabulating means and frequency distributions for the 2020 surveys and comparing the results to prior years. The data from these surveys are then used in a stock‐and‐flow supply projection model to learn the extent to which RN shortages might emerge in the future. Population Studied Registered nurses and nursing education programs in California. Principal Findings Approximately 2000 RNs responded to the biennial Survey by November 2020. The data indicate that: (1) employment rates of older nurses dropped substantially: 6 percentage points for RNs 60‐64 years old and 10 percentage points for RNs 65 years and older. Employment rates for nurses younger than 30 years also dropped, but not significantly. Employment of nurses 30‐49 years increased approximately 6 percentage points, making up for the decreases of older RNs. Among RNs 55‐64 years old, the percent reporting they intend to retire or leave nursing within two years increased from 11.4% in 2018 to 24.5% in 2020. The survey of RN education programs finds that 16 of California's 147 nursing programs skipped a cohort of students in 2020 and another 18 programs enrolled fewer students than the prior academic year. The estimated decrease in students statewide is approximately 350 ‐ 2.3% of ~15,000 new enrollments in 2018‐2019. Conclusions Although RN enrollments decreased only negligibly, the rapid decrease in employment of older nurses and increase in projected retirements suggest that RN shortages may rapidly emerge. Implications for Policy or Practice Over the past five years, hospitals have been increasingly uninterested in hiring newly‐graduated nurses, even while reporting shortages of experienced RNs. Hospitals need to rapidly hire newly‐graduated RNs in order to compensate for the rapid outflow of older RNs from the labor supply.
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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 | 0.004 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".