Job Flows Into and Out of Health Care Before and After the COVID-19 Pandemic
Why this work is in the frame
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Bibliographic record
Abstract
Importance: Anecdotal evidence suggests that health care employers have faced increased difficulty recruiting and retaining staff in the wake of the COVID-19 pandemic. Empirical research is needed to understand the magnitude and persistence of these changes, and whether they have disproportionate implications for certain types of workers or regions of the country. Objective: To quantify the number of workers exiting from and entering into the health care workforce before and after the pandemic and to examine variations over time and across states and worker demographics. Design, Setting, and Participants: This cohort study used US Census Bureau state unemployment insurance data on job-to-job flows in the continental US to construct state-level quarterly exit and entry rates for the health care industry from January 2018 through December 2021 (Arkansas, Mississippi, and Tennessee were omitted due to missing data). An event study design was used to compute quarterly mean adjusted rates of job exit from and entry into the health care sector as defined by the North American Industry Classification System. Data were examined from January to June 2023. Exposure: The COVID-19 pandemic. Main Outcomes and Measures: The main outcomes were the mean adjusted health care worker exit and entry rates in each quarter by state and by worker demographics (age, gender, race and ethnicity, and education level). Results: In quarter 1 of 2020, there were approximately 18.8 million people (14.6 million females [77.6%]) working in the health care sector in our sample. The exit rate for health care workers increased at the onset of the pandemic, from a baseline quarterly mean of 5.9 percentage points in 2018 to 8.0 (95% CI, 7.7-8.3) percentage points in quarter 1 of 2020. Exit rates remained higher than baseline levels through quarter 4 of 2021, when the health care exit rate was 7.7 (95% CI, 7.4-7.9) percentage points higher than the 2018 baseline. In quarter 1 of 2020, the increase in health care worker exit rates was dominated by an increase in workers exiting to nonemployment (78% increase compared with baseline); in contrast, by quarter 4 of 2021, the exit rate was dominated by workers exiting to employment in non-health care sectors (38% increase compared with baseline). Entry rates into health care also increased in the postpandemic period, from 6.2 percentage points at baseline to 7.7 percentage points (95% CI, 7.4-7.9 percentage points) in the last quarter of 2021, suggesting increased turnover of health care staff. Compared with prepandemic job flows, the share of workers exiting health care after the pandemic who were female was disproportionately larger, and the shares of workers entering health care who were female or Black was disproportionately smaller. Conclusions and Relevance: Results of this cohort study suggest a substantial and persistent increase in health care workforce turnover after the pandemic, which may have long-lasting implications for workers' willingness to remain in health care jobs. Policymakers and health care organizations may need to act to prevent further losses of experienced staff.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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