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SDG partnerships may perpetuate the global North-South divide.

ABSTRACT: The 2030 Agenda for Sustainable Development gives equal emphasis to developed ("Northern") countries and developing ("Southern") countries. Thus, implementation of the Sustainable Development Goals (SDGs) demands coherent collaboration to transform society across all countries. Yet, there has been little research published on SDG partnerships and this is the first study to explore the extent to which partners from Northern and Southern countries are involved in them and their focus. It identifies that involvement is unequally distributed and may perpetuate the North-South divide in countries' resources, including access to data and scientific capacities. Most notably, partners from low-income countries are involved in far fewer partnerships than partners from countries in all other World Bank income categories, although the former are least able to develop sustainably. As such, all those promoting sustainable development from governmental, private and third-sector organisations need to address global inequalities in establishing and implementing SDG partnerships if, collectively, they are to facilitate delivery of Agenda 2030.

SUBMITTER: Blicharska M

PROVIDER: S-EPMC8617181 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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SDG partnerships may perpetuate the global North-South divide.

Blicharska Malgorzata M Teutschbein Claudia C Smithers Richard J RJ

Scientific reports 20211125 1

The 2030 Agenda for Sustainable Development gives equal emphasis to developed ("Northern") countries and developing ("Southern") countries. Thus, implementation of the Sustainable Development Goals (SDGs) demands coherent collaboration to transform society across all countries. Yet, there has been little research published on SDG partnerships and this is the first study to explore the extent to which partners from Northern and Southern countries are involved in them and their focus. It identifie ...[more]

PMID: 34824306

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Project description:At their best, research partnerships provide a mechanism to optimize each partner's strengths, make scientific discoveries and achieve development goals. Each partner stands to gain from the relationship and perceives it to be fair. However, partnerships between institutions in the global North and the global South have been beleaguered by structural inequalities and power imbalances, and Northern stakeholders have been criticized for perpetuating paternalistic or neo-colonial behaviours. As part of efforts to redress imbalances and achieve equity and mutual benefit, various principles, guidelines, frameworks and models for partnership have been developed. This scoping review maps the literature and summarizes key features of the guidelines for North-South research partnerships. The review was conducted between October 2020 and January 2021. Three academic journal databases and Google were searched, and additional resources were identified through a hand search of reference lists and expert recommendation. Twenty-two guidelines were identified published between 1994 and 2021 and originating predominantly in the fields of international development and global health. The themes addressed within the guidelines were aggregated using NVivo qualitative analysis software to code the content of each guideline. Topics featuring most prominently in the guidelines were: partner roles, responsibilities and ways of working; capacity strengthening; motivation and goals; resource contributions; agenda setting and study design; governance structures and institutional agreements; dissemination; respect for affected populations; data handling and ownership; funding and long-term commitments. The current study reinforces many of the themes from two recent scoping reviews specific to the field of global health, but gaps remain, which need to be addressed: Southern stakeholders continue to be under-represented in guideline development, and there is limited evidence of how guidelines are used in practice. Further exploration is needed of Southern stakeholder priorities and whether and how guidelines are operationalized.

Project description:BackgroundWhile artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities.MethodsWe performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model, leveraging transfer-learning techniques (building upon an existing BioBERT model) to predict eligibility for inclusion (original, human, clinical AI literature). Of all eligible articles, database country source and clinical specialty were manually labelled. A BioBERT-based model predicted first/last author expertise. Author nationality was determined using corresponding affiliated institution information using Entrez Direct. And first/last author sex was evaluated using the Gendarize.io API.ResultsOur search yielded 30,576 articles, of which 7,314 (23.9%) were eligible for further analysis. Most databases came from the US (40.8%) and China (13.7%). Radiology was the most represented clinical specialty (40.4%), followed by pathology (9.1%). Authors were primarily from either China (24.0%) or the US (18.4%). First and last authors were predominately data experts (i.e., statisticians) (59.6% and 53.9% respectively) rather than clinicians. And the majority of first/last authors were male (74.1%).InterpretationU.S. and Chinese datasets and authors were disproportionately overrepresented in clinical AI, and almost all of the top 10 databases and author nationalities were from high income countries (HICs). AI techniques were most commonly employed for image-rich specialties, and authors were predominantly male, with non-clinical backgrounds. Development of technological infrastructure in data-poor regions, and diligence in external validation and model re-calibration prior to clinical implementation in the short-term, are crucial in ensuring clinical AI is meaningful for broader populations, and to avoid perpetuating global health inequity.

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SDG partnerships may perpetuate the global North-South divide.

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SDG partnerships may perpetuate the global North-South divide.

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