POST COVID-19 PANDEMIC EMPLOYMENT RECOVERY BY HEALTHCARE SECTOR IN US RACIAL MAJORITY-MINORITY COUNTIES
Bibliographic record
Abstract
Abstract Health care sector employment trends following the COVID-19 pandemic have revealed a labor force participation gap. Racial/ethnic composition differences between counties may contribute to an uneven labor market recovery. This study’s objectives were to compare post-pandemic employment recovery between five health care sectors (ambulatory services (AS), home health care (HHC), nursing and residential care (NRC), assisted living (AL), nursing facilities/homes (NH)), and to examine whether health care sector recovery differences exist between racial majority-minority (MM) and non-racial MM counties in the USA. First Quarter 2020 and fourth Quarter 2022 data from the US Bureau of Labour Statistics Quarterly Census of Employment and Wages (QCEW) were used to calculate cumulative percentage change in health care sector employment. The Race Origin Variables from the latest 2021 data from the American Community Survey were used to identify racial MM counties. The sector with the greatest number of counties with positive employment recovery was the AS sector in 1562/3176 (49%) counties, and 252/468 (54%) MM counties. The NH sector had the lowest number of counties with positive employment recovery in 170/2818 (6%) counties, and 34/389 (9%) MM counties. This is the first analysis of post-pandemic health care sector employment recovery examining MM counties. Differences in county and state policies may account for varying degrees of recovery. Further research is necessary for the development of best practice guidelines and policies to improve post-pandemic health care employment recovery and the quality of nursing home care among older adults.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".