Changes in Registered Nurse Employment and Education Capacity during the COVID‐19 Pandemic and the Risk of Future Shortages
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».