ABSTRACT
Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because new technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces labor en masse. Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only can we accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19). They can be leveraged by policy-makers, educators, and job seekers who are forced to confront the often distressing challenges of finding new jobs.
Subject(s)
Algorithms , Employment , Professional Competence , Vocational Guidance/methods , Australia/epidemiology , COVID-19/epidemiology , Datasets as Topic , Demography , Humans , Industry/methods , Industry/organization & administration , Industry/statistics & numerical data , Occupations/statistics & numerical data , Pandemics , Population Dynamics , Professional Competence/statistics & numerical data , Vocational Guidance/organization & administration , Vocational Guidance/statistics & numerical dataABSTRACT
National efforts are underway to prepare the UK National Health Service (NHS) for the COVID-19 pandemic; however, the efficacy of these interventions is unknown. In view of this, a cross-sectional survey of front-line healthcare workers (HCWs) at two large acute NHS hospital trusts in England was undertaken to assess their confidence and perceived level of preparedness for the virus. The survey found that there has been moderate success in readying HCWs to manage COVID-19, but that more still needs to be done, particularly in relation to educating HCWs about laboratory diagnostics.
Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/diagnosis , Coronavirus Infections/therapy , Disease Management , Health Knowledge, Attitudes, Practice , Health Personnel/psychology , Pneumonia, Viral/diagnosis , Pneumonia, Viral/therapy , Professional Competence/statistics & numerical data , COVID-19 , Cross-Sectional Studies , England , Hospitals , Humans , Pandemics , SARS-CoV-2ABSTRACT
The study analysed healthcare workers' (HCWs) knowledge, practices, and attitudes regarding coronavirus disease 2019 (COVID-19). A cross-sectional survey was conducted from February 4th to February 8th, 2020, involving a total of 1357 HCWs across 10 hospitals in Henan, China. Of those surveyed, 89% of HCWs had sufficient knowledge of COVID-19, more than 85% feared self-infection with the virus, and 89.7% followed correct practices regarding COVID-19. In addition to knowledge level, some risk factors including work experience and job category influenced HCWs' attitudes and practice concerning COVID-19. Measures must be taken to protect HCWs from risks linked to job category, work experience, working hours, educational attainment, and frontline HCWs.