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1000 Titel
  • Recruiting refugees to reduce labour shortages in health care professions: experimental evidence on the potential of foreign-language outreach on social media
1000 Autor/in
  1. Tjaden, Jasper |
  2. Seuthe, Miriam |
  3. Weinert, Sebastian |
1000 Verlag
  • BioMed Central
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-07-03
1000 Erschienen in
1000 Quellenangabe
  • 22(1):48
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s12960-024-00933-w |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11223288/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>Many high-income countries are grappling with severe labour shortages in the healthcare sector. Refugees and recent migrants present a potential pool for staff recruitment due to their higher unemployment rates, younger age, and lower average educational attainment compared to the host society's labour force. Despite this, refugees and recent migrants, often possessing limited language skills in the destination country, are frequently excluded from traditional recruitment campaigns conducted solely in the host country’s language. Even those with intermediate language skills may feel excluded, as destination-country language advertisements are perceived as targeting only native speakers. This study experimentally assesses the effectiveness of a recruitment campaign for nursing positions in a German care facility, specifically targeting Arabic and Ukrainian speakers through Facebook advertisements.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>We employ an experimental design (AB test) approximating a randomized controlled trial, utilizing Facebook as the delivery platform. We compare job advertisements for nursing positions in the native languages of Arabic and Ukrainian speakers (treatment) with the same advertisements displayed in German (control) for the same target group in the context of a real recruitment campaign for nursing jobs in Berlin, Germany. Our evaluation includes comparing link click rates, visits to the recruitment website, initiated applications, and completed applications, along with the unit cost of these indicators. We assess statistical significance in group differences using the Chi-squared test.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>We find that recruitment efforts in the origin language were 5.6 times (Arabic speakers) and 1.9 times (Ukrainian speakers) more effective in initiating nursing job applications compared to the standard model of German-only advertisements among recent migrants and refugees. Overall, targeting refugees and recent migrants was 2.4 (Ukrainians) and 10.8 (Arabic) times cheaper than targeting the reference group of German speakers indicating higher interest among these groups.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusions</jats:title> <jats:p>The results underscore the substantial benefits for employers in utilizing targeted recruitment via social media aimed at foreign-language communities within the country. This strategy, which is low-cost and low effort compared to recruiting abroad or investing in digitalization, has the potential for broad applicability in numerous high-income countries with sizable migrant communities. Increased employment rates among underemployed refugee and migrant communities, in turn, contribute to reducing poverty, social exclusion, public expenditure, and foster greater acceptance of newcomers within the receiving society.</jats:p> </jats:sec>
1000 Sacherschließung
lokal Female [MeSH]
lokal Adult [MeSH]
lokal Language [MeSH]
lokal Advertising/methods [MeSH]
lokal Humans [MeSH]
lokal Refugees [MeSH]
lokal Migrant
lokal Middle Aged [MeSH]
lokal Nursing
lokal Social Media/statistics
lokal Advertising/statistics
lokal Male [MeSH]
lokal Research
lokal Germany [MeSH]
lokal Refugee
lokal Personnel Selection [MeSH]
lokal Recruitment
lokal Arabs [MeSH]
lokal Transients and Migrants [MeSH]
lokal Facebook
lokal Health Personnel [MeSH]
lokal Social media
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-5054-3535|https://frl.publisso.de/adhoc/uri/U2V1dGhlLCBNaXJpYW0=|https://frl.publisso.de/adhoc/uri/V2VpbmVydCwgU2ViYXN0aWFu
1000 Hinweis
  • DeepGreen-ID: 4106e5f30fda493e83aa49a4295f30a2 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
1000 Label
1000 Förderer
  1. Fuerst Donnersmark Foundation |
  2. Universität Potsdam |
1000 Fördernummer
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1000 Förderprogramm
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  2. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Fuerst Donnersmark Foundation |
    1000 Förderprogramm -
    1000 Fördernummer -
  2. 1000 joinedFunding-child
    1000 Förderer Universität Potsdam |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
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1000 @id frl:6523588.rdf
1000 Erstellt am 2025-07-06T22:43:00.266+0200
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