COVID-19, jobs and skills—Exploratory analysis of the job postings in the US and UK healthcare job market
The COVID-19 pandemic has significantly affected all spheres of life, including the healthcare workforce. While the COVID-19 pandemic has started driving organizational and societal shifts, it is vital for healthcare organizations and decision-makers to analyze patterns in the changing workforce. In...
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| Veröffentlicht in: | PloS one Jg. 18; H. 1; S. e0278237 |
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20.01.2023
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| Abstract | The COVID-19 pandemic has significantly affected all spheres of life, including the healthcare workforce. While the COVID-19 pandemic has started driving organizational and societal shifts, it is vital for healthcare organizations and decision-makers to analyze patterns in the changing workforce. In this study, we aim to identify patterns in healthcare job postings during the pandemic to understand which jobs and associated skills are trending after the advent of COVID-19. Content analysis of job postings was conducted using data-driven approaches over two-time intervals in the pandemic. The proposed framework utilizes Latent Dirichlet Allocation (LDA) for topic modeling to evaluate the patterns in job postings in the US and the UK. The most demanded jobs, skills and tasks for the US job postings are presented based on job posting data from popular job posting websites. This is obtained by mapping the job postings to the jobs, skills and tasks defined in the O*NET database for the healthcare occupations in the US. The topic modeling results clearly show increased hiring for telehealth services in both the US and UK. This study also presents an increase in demand for specific occupations and skills in the USA healthcare industry. The results and methods used in the study can help monitor rapid changes in the job market due to pandemics and guide decision-makers to make organizational shifts in a timely manner. |
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| AbstractList | The COVID-19 pandemic has significantly affected all spheres of life, including the healthcare workforce. While the COVID-19 pandemic has started driving organizational and societal shifts, it is vital for healthcare organizations and decision-makers to analyze patterns in the changing workforce. In this study, we aim to identify patterns in healthcare job postings during the pandemic to understand which jobs and associated skills are trending after the advent of COVID-19. Content analysis of job postings was conducted using data-driven approaches over two-time intervals in the pandemic. The proposed framework utilizes Latent Dirichlet Allocation (LDA) for topic modeling to evaluate the patterns in job postings in the US and the UK. The most demanded jobs, skills and tasks for the US job postings are presented based on job posting data from popular job posting websites. This is obtained by mapping the job postings to the jobs, skills and tasks defined in the O*NET database for the healthcare occupations in the US. The topic modeling results clearly show increased hiring for telehealth services in both the US and UK. This study also presents an increase in demand for specific occupations and skills in the USA healthcare industry. The results and methods used in the study can help monitor rapid changes in the job market due to pandemics and guide decision-makers to make organizational shifts in a timely manner. The COVID-19 pandemic has significantly affected all spheres of life, including the healthcare workforce. While the COVID-19 pandemic has started driving organizational and societal shifts, it is vital for healthcare organizations and decision-makers to analyze patterns in the changing workforce. In this study, we aim to identify patterns in healthcare job postings during the pandemic to understand which jobs and associated skills are trending after the advent of COVID-19. Content analysis of job postings was conducted using data-driven approaches over two-time intervals in the pandemic. The proposed framework utilizes Latent Dirichlet Allocation (LDA) for topic modeling to evaluate the patterns in job postings in the US and the UK. The most demanded jobs, skills and tasks for the US job postings are presented based on job posting data from popular job posting websites. This is obtained by mapping the job postings to the jobs, skills and tasks defined in the O*NET database for the healthcare occupations in the US. The topic modeling results clearly show increased hiring for telehealth services in both the US and UK. This study also presents an increase in demand for specific occupations and skills in the USA healthcare industry. The results and methods used in the study can help monitor rapid changes in the job market due to pandemics and guide decision-makers to make organizational shifts in a timely manner.The COVID-19 pandemic has significantly affected all spheres of life, including the healthcare workforce. While the COVID-19 pandemic has started driving organizational and societal shifts, it is vital for healthcare organizations and decision-makers to analyze patterns in the changing workforce. In this study, we aim to identify patterns in healthcare job postings during the pandemic to understand which jobs and associated skills are trending after the advent of COVID-19. Content analysis of job postings was conducted using data-driven approaches over two-time intervals in the pandemic. The proposed framework utilizes Latent Dirichlet Allocation (LDA) for topic modeling to evaluate the patterns in job postings in the US and the UK. The most demanded jobs, skills and tasks for the US job postings are presented based on job posting data from popular job posting websites. This is obtained by mapping the job postings to the jobs, skills and tasks defined in the O*NET database for the healthcare occupations in the US. The topic modeling results clearly show increased hiring for telehealth services in both the US and UK. This study also presents an increase in demand for specific occupations and skills in the USA healthcare industry. The results and methods used in the study can help monitor rapid changes in the job market due to pandemics and guide decision-makers to make organizational shifts in a timely manner. |
| Audience | Academic |
| Author | Maalouf, Maher Omar, Mohammed Upadhyay, Himanshu Anwar, Siddiq Simsekler, Mecit Can Emre |
| AuthorAffiliation | 2 Department of Medicine, Sheikh Shakbout Medical City, Abu Dhabi, United Arab Emirates 1 Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates University of Georgia, UNITED STATES |
| AuthorAffiliation_xml | – name: 1 Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates – name: University of Georgia, UNITED STATES – name: 2 Department of Medicine, Sheikh Shakbout Medical City, Abu Dhabi, United Arab Emirates |
| Author_xml | – sequence: 1 givenname: Himanshu orcidid: 0000-0001-8464-8717 surname: Upadhyay fullname: Upadhyay, Himanshu – sequence: 2 givenname: Mecit Can Emre orcidid: 0000-0002-1555-5012 surname: Simsekler fullname: Simsekler, Mecit Can Emre – sequence: 3 givenname: Maher orcidid: 0000-0003-0516-6870 surname: Maalouf fullname: Maalouf, Maher – sequence: 4 givenname: Siddiq surname: Anwar fullname: Anwar, Siddiq – sequence: 5 givenname: Mohammed surname: Omar fullname: Omar, Mohammed |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36662704$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1080_17517575_2025_2521389 crossref_primary_10_1002_joe_22272 |
| Cites_doi | 10.1145/2987538.2987542 10.1089/tmj.2020.29040.rb 10.1515/crll.1909.136.210 10.1093/oxrep/graa033 10.1007/s00500-019-04247-1 10.1016/j.is.2016.10.009 10.1093/jamia/ocaa072 10.3390/ijerph17145112 10.1093/jamia/ocaa067 |
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| Copyright | Copyright: © 2023 Upadhyay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2023 Public Library of Science 2023 Upadhyay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023 Upadhyay et al 2023 Upadhyay et al 2023 Upadhyay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Biology and Life Sciences Content analysis Coronaviruses COVID-19 COVID-19 - epidemiology Data collection Data mining Decision analysis Decision making Delivery of Health Care Dictionaries Dirichlet problem Employment Epidemics Evaluation Forecasts and trends Health care Health care industry Health Care Sector Humans Job hunting Labor market Medicine and Health Sciences Modelling Occupations Pandemics People and Places Skills Social Sciences Statistics Telemedicine United Kingdom United Kingdom - epidemiology Websites |
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| Title | COVID-19, jobs and skills—Exploratory analysis of the job postings in the US and UK healthcare job market |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/36662704 https://www.proquest.com/docview/2767425447 https://www.proquest.com/docview/2768235180 https://pubmed.ncbi.nlm.nih.gov/PMC9858305 https://doaj.org/article/d991b518daf74a04b16c52766028f04d http://dx.doi.org/10.1371/journal.pone.0278237 |
| Volume | 18 |
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