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Further Education and Training in ICT: Inequalities Among European Workers Before and After the COVID‐19 Outbreak

Martin Lakomý et al.

International Journal of Training and Development2026https://doi.org/10.1111/ijtd.70020article
AJG 2ABDC C
Weight
0.50

What the paper says

ABSTRACT Further education and training (FET) should form a foundation for increasingly dynamic and digital European economies. Policies aim to support ongoing workforce development and retain as many individuals as possible in the labour market. However, limited existing evidence supports the Matthew effect in this area—FET tends to be more accessible for workers with higher qualifications in better jobs. This study uses data from the EU Labour Force Survey and examines the relationship between individual and job characteristics with FET, as well as its shift during the COVID‐19 outbreak. The findings confirm that FET is more common among younger and middle‐aged workers, those in higher‐level positions, workers in larger companies, and those not working under a supervisor. Some of these patterns changed with the onset of the pandemic, notably the link between FET in ICT and higher education becoming stronger. This study identifies several robust factors that influence participation in FET, serving as a foundation for policies that aim to provide accessible, targeted FET opportunities for groups at risk of exclusion in a digitalised labour market.

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https://doi.org/https://doi.org/10.1111/ijtd.70020

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@article{martin2026,
  title        = {{Further Education and Training in ICT: Inequalities Among European Workers Before and After the COVID‐19 Outbreak}},
  author       = {Martin Lakomý et al.},
  journal      = {International Journal of Training and Development},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/ijtd.70020},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.