Evidence map›Paper›PMID 37483560›Full record

ArticleJournal of patient-centered research and reviews2023

Examining Racial Disparities in Unemployment Among Health Care Workers Before, During, and After the COVID-19 Pandemic.

Jason Semprini

Open access · diamondAbstract read
In one paragraph

Article in Journal of patient-centered research and reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Jason SempriniUniversity of Iowa College of Public Health, Iowa City, IA.
University of Iowa · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Among the U.S. health care workforce, the COVID-19 pandemic appeared to greatly impact employment levels in 2020. However, no research has examined how the pandemic's impact on employment varied by racial/ethnic group or beyond the initial emergency year. Our study aimed to quantitatively evaluate workforce trends by race/ethnicity before, during, and after the COVID-19 pandemic. This study analyzed each March supplement of the Current Population Survey over a 5-year span (2018-2022). We restricted the sample to nurses, physician assistants, and other non-physician health care workers (HCW), per specific census occupation codes, and constructed an event-history study to test for differential effects from each year, as compared to 2019, on the proportion of employment between non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Native (American Indian, Alaska Native, Hawaiian Islander), and non-Hispanic Asian HCW. Results suggest that the pandemic's negative impact on the health care workforce disproportionately reduced employment for HCW self-identifying as Black or Indigenous. Rates for other groups increased 2-3 percentage points in 2020 but returned to prepandemic levels by 2022. However, for Black and Native HCW, the change was twice as large in 2021 and remained significantly higher in 2022 for Black HCW, providing more evidence that the burden of the COVID-19 pandemic disproportionately fell on people of color. Future research investigating how employment disruptions impacted the health care workforce and, potentially, health equity remains warranted.

Indexed as

advanced practitionersCOVID-19health equitymedical assistantsnursesnursing aidesphysician assistantsracial disparitiesworkforce

Identifiers

PMID37483560
PMCPMC10358973
OpenAlexW4384697269

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.