Policy learning and universal health coverage in low- and middle-income countries

• Verfasst von: Kiendrébéogo, Joël [VerfasserIn]
• Verfasst von: De Allegri, Manuela [VerfasserIn]
• Verfasst von: Meessen, Bruno [VerfasserIn]
• Titel: Policy learning and universal health coverage in low- and middle-income countries
• Verf.angabe: Joël Arthur Kiendrébéogo, Manuela De Allegri and Bruno Meessen
• E-Jahr: 2020
• Jahr: 21 July 2020
• Umfang: 11 S.
• Fussnoten: Gesehen am 15.10.2020
• Titel Quelle: Enthalten in: Health research policy and systems
• Ort Quelle: London : BioMed Central, 2003
• Jahr Quelle: 2020
• Band/Heft Quelle: 18(2020), Artikel-ID 85, Seite 1-11
• ISSN Quelle: 1478-4505
• DOI: doi:10.1186/s12961-020-00591-z
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1735687987

Abstract

Learning is increasingly seen as an essential component to spur progress towards universal health coverage (UHC) in low- and middle-income countries (LMICs). However, learning remains an elusive concept, with different understandings and uses that vary from one person or organisation to another. Specifically, it appears that ‘learning for UHC’ is dominated by the teacher mode — notably scientists and experts as ‘teachers’ conveying to local decision/policy-makers as ‘learners’ what to do. This article shows that, to meet countries’ needs, it is important to acknowledge that UHC learning situations are not restricted to the most visible epistemic learning approach practiced today. This article draws on an analytical framework proposed by Dunlop and Radaelli, whereby they identified four learning modes that can emerge according to the specific characteristics of the policy process: epistemic learning, learning in the shadow of hierarchy, learning through bargaining and reflexive learning. These learning modes look relevant to help widen the learning prospects that LMICs need to advance their UHC agenda. Actually, they open up new perspectives in a research field that, until now, has appeared scattered and relatively blurry.