Unravelling the complexity of research capacity strengthening for health professionals in low- and middle-income countries: A concept analysis

Exploring the landscape of essential health data science skills and research challenges: a survey of stakeholders in Africa, Asia, and Latin America and the Caribbean

Background

Data science approaches have been pivotal in addressing public health challenges. However, there has been limited focus on identifying essential data science skills for health researchers, gaps in capacity building provision, barriers to access, and potential solutions.

Objectives

This review aims to identify essential data science skills for health researchers and key stakeholders in Africa, Asia, and Latin America and the Caribbean (LAC), as well as to explore gaps and barriers in data science capacity building and share potential solutions, including any regional variations.

Methods

An online survey was conducted in English, French, Spanish and Portuguese, gathering both quantitative and qualitative responses. Descriptive analysis was performed in R V4.3, and a thematic workshop approach facilitated qualitative analysis.

Findings

From 262 responses from individuals across 54 low- and middle-income countries (LMICs), representing various institutions and roles, we summarised essential data science skills globally and by region. Thematic analysis revealed key gaps and barriers in capacity building, including limited training resources, lack of mentoring, challenges with data quality, infrastructure and privacy issues, and the absence of a conducive research environment.

Conclusion and future directions

Respondents' consensus on essential data science skills suggests the need for a standardised framework for capacity building, adaptable to regional contexts. Greater investment, coupled with expanded collaboration and networking, would help address gaps and barriers, fostering a robust data science ecosystem and advancing insights into global health challenges.

Articulating the ultimate objectives of research capacity strengthening programmes: Why this is important and how we might achieve it

'Research capacity strengthening' (RCS) is an umbrella term that can be used to describe a wide variety of activities conducted in support of diverse objectives premised upon distinct, potentially opposing, views. Despite this, the ultimate objective of RCS activities is rarely made explicit which can be problematic when diverse objectives are possible. By 'ultimate' objective we are referring to the overarching (often long-term) goal an RCS initiative is intended to contribute towards (e.g. better population health) as opposed to the more immediate 'proximate' (often short-term) objectives of any such activity (e.g. improved capacity to undertake infectious disease research). We argue a need for those funding, designing and implementing RCS initiatives to make clear statements as to the ultimate objective that they foresee their respective initiative contributing towards as well as the proposed pathway and associated assumptions that underlie their approach. Examples of distinct ultimate objectives for RCS initiatives are presented alongside fictitious examples of how they may be transparently reported from both a funder and implementor perspective. Such transparency should be routine within the scope of funding calls for RCS activities (even when such activities are only a minor component of the call), subsequent applications to those calls and any description of an applied RCS activity/ies and/or the associated outcomes thereof. The process of determining one's ultimate objective will further cause funders and actors to think through their respective initiatives more thoroughly and make informed choices and better designed RCS projects. Doing so would reduce any ambiguity associated with the use of the term 'research capacity strengthening' and would provide a stronger foundation for robust programme evaluation.