Evidence map›Paper›PMID 36649272›Full record

ArticlePloS one2023

Digitalisation of occupations-Developing an indicator based on digital skill requirements.

Carolina Lennon, Laura Samantha Zilian, Stella Sophie Zilian

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Carolina LennonGraz Schumpeter Centre, University of Graz, Graz, Austria.
Laura Samantha ZilianGraz Schumpeter Centre, University of Graz, Graz, Austria.ORCID 0000-0002-2957-0678
Stella Sophie ZilianGraz Schumpeter Centre, University of Graz, Graz, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digitalisation is assumed to have far reaching consequences for workers. So far, these have been analysed using indicators derived from survey data on occupational tasks. Survey-based indicators measure what people do at work but provide little insight into the skills required to perform a task. Since multiple skills may be necessary to perform a task, approximating digital skills through tasks may underestimate the extent of digitalisation of a given occupation. Besides, they provide limited coverage in terms of periodicity, scope and variety of tasks. We therefore suggest to change the perspective from tasks to skills and propose to analyse the digital skill requirements of occupations. To this end, we use detailed information on the classification of European Occupations, Skills and Qualifications, natural language processing tools and network analysis methods to determine digital skills in the database. We construct four different versions of the digital competencies indicator identifying occupations that depend highly on digital skills. Our indicator can be mapped to the ISCO-08 classification and easily be used alongside other data sources. We show that compared to an indicator based on ICT-tasks derived from the OECD 'Programme for the Assessment of Adult Skills', our indicator captures more complex and specialised digitalised occupations. Our results stress the importance of using granular data in order to properly identify digital skill requirements of jobs.

Indexed as

OccupationsAdultHumansRecordsSurveys and Questionnaires

Identifiers

PMID36649272
PMCPMC9844856

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.