1489 Evaluation of a multi-professional paediatric training programme in a global health partnership

Background: Public-private partnerships (PPPs) in global health are increasingly common to support sustainable development and strengthen health systems in low- and middle-income countries. Since the release of the Sustainable Development Goals (SDGs) in 2015 culminating in a discrete goal "to revitalize the global partnership for sustainable development," public health scholars have sought to understand what makes PPPs successful in different contexts. While trust has long been identified as a key component of successful strategic alliances in the private sector, less is known about how trust emerges between public- and private- sector partners, particularly in global health. Therefore, we investigated how trust between partners evolved in the context of Project Last Mile (PLM), a global health partnership that translates the business acumen of The Coca-Cola Company to strengthen public health systems across Africa. Methods: This study draws upon secondary analysis of qualitative data generated as part of the longitudinal, mixed methods evaluation of PLM across country settings. Seventy-seven interviews with a purposeful sample of key stakeholders were conducted in Mozambique, South Africa and eSwatini between August 2016 and July 2018. Trained qualitative interviewers followed a standard discussion guide, and audio-recorded interviews with participants’ consent. In this secondary analysis, we analyzed qualitative data to understand how trust between partners was cultivated across settings. Results: We drew upon stakeholder experiences to inform an inductive framework for how trust develops over time. Our analysis revealed five domains that were foundational to building trust: (1) reputational context, (2) team composition, (3) tangible outputs, (4) shared values, and (5) effective communication. Conclusion: The framework may be useful for private and public sector entities seeking to establish and sustain trust within their global health partnerships.

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Characterizing a community health partnership in Dominican Republic: Network mapping and analysis of stakeholder perceptions

Canadian Medical Education Journal ◽

10.36834/cmej.36924 ◽

2018 ◽

Vol 9 (2) ◽

pp. e60-71

Author(s):

Kristy Yiu ◽

Helen Dimaras ◽

Olga Valdman ◽

Bido Franklin ◽

John Prochaska ◽

...

Keyword(s):

Network Analysis ◽

Global Health ◽

Community Health ◽

Analysis Tool ◽

Community Needs ◽

Health Partnership ◽

Community Health Partnership ◽

Curricular Elements ◽

Network Mapping ◽

Qualitative Analyses

Background: Medical trainees complete learning experiences abroad to fulfil global health curricular elements, but this participation has been steadily criticized as fulfilling learner objectives at the cost of host communities. This study uses network and qualitative analyses in characterizing a community coalition in order to better understand its various dimensions and to explore the perceived benefits it provided towards optimizing community outcomes.Methods: Data from a semi-structured survey was used for network and qualitative analyses. Partner linkages were assessed using network analysis tool UCINET 6 (version 6.6). Thematic analysis was conducted on qualitative responses around the perceived coalition strengths and weaknesses.Results: Network analysis confirmed that local member organizations were key network influencers based on reported formal agreements, general interactions, and information shared. While sharing of resources was rare, qualitative analysis suggested that information sharing contributed to engagement, enthusiasm, and communication that allowed visiting partners to expand their understanding of community needs and shift their focus beyond learner objectives.Conclusion: Global health programs for medical students should consider the use of community health coalitions to optimally align the work undertaken by learners on global health experiences abroad. Network mapping can help educators and coalition partners visualize interactions and identify value.

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