Evidence map›Paper›PMID 38985717›Full record

ArticlePloS one2024

Standardising job descriptions in the humanitarian supply chain: A text mining approach for recruitment process.

Irene Spada, Valeria Fabbroni, Filippo Chiarello, Gualtiero Fantoni

Abstract read
In one paragraph

Article in PloS one, 2024. 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

4 authors.

Irene SpadaDepartment of Energy, Systems, Land and Construction Engineering, School of Engineering, University of Pisa, Pisa, Italy.ORCID 0000-0002-7576-8255
Valeria FabbroniDepartment of Civilisations and Forms of Knowledge, University of Pisa, Pisa, Italy.
Filippo ChiarelloDepartment of Energy, Systems, Land and Construction Engineering, School of Engineering, University of Pisa, Pisa, Italy.
Gualtiero FantoniBusiness Engineering for Data Science (B4DS) Research Lab, School of Engineering, University of Pisa, Pisa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeUncertainty and complexity have increased in recent decades, posing new challenges to humanitarian organisations. This study investigates whether using standard terminology in Human Resource Management processes can support the Humanitarian supply chain in attracting and maintaining highly skilled operators. METHODOLOGY: We exploit text mining to compare job vacancies on ReliefWeb, the reference platform for humanitarian job seekers, and ESCO, the European Classification of Skills, Competencies, and Occupations. We measure the level of alignment in these two resources, providing quantitative evidence about terminology standardisation in job descriptions for supporting HR operators in the Humanitarian field.

findingsThe most in-demand skills, besides languages, relate to resource management and economics and finance for capital management. Our results show that job vacancies for managerial and financial profiles are relatively more in line with the European database than those for technical profiles. However, the peculiarities of the humanitarian sector and the lack of standardisation are still a barrier to achieving the desired level of coherence with humanitarian policies.

Indexed as

Data MiningAltruismHumansJob DescriptionPersonnel SelectionRelief Work

Identifiers

PMID38985717
PMCPMC11236101

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.