Analysis and data sharing

Analysing data throws up a number of ethical dilemmas for health systems researchers; many of which have already been raised and discussed in the earlier sections of this resource, including having to carefully consider the physical and socio-political positions of researchers which have implications for the entire research process, including analysis, and the importance of considering collaborators and research impact in analysis plans.

An important consideration for all health research, and particularly for an applied field like health systems research, is to ensure that findings feed into more equitable policy and practice in health systems; to ensure maximum social benefit from the research. The research-policy-practice interface at local, national and global levels is far from straight-forward however, with significant time and proactive effort needed to ensure that research findings make their way into the world of policy making. At local and national levels, having research findings heard is often dependent on relationships initiated long before and far after the completion of research.

Ghaffar et al (2017), highlight the value of embedded research approaches to strengthening the uptake of research findings in policy and practice. They highlight the importance of developing institutional set-ups and relationships that contribute to local transformation. Theobald and Nhlema-Simwaka (2008) explore the role of applied social science research in encouraging the uptake and use of research findings in Malawi. The paper by Gilson and McIntyre (2008) draws on research from Kenya and South Africa and prompts reflection on the ways in which policy makers can utilize research evidence as well as the importance of trusting relationships in this exchange.

A related consideration that is gaining growing attention globally is about sharing of data, including if and how to allow others to access and analyse data. Sharing data can support the impact of health system research through ensuring that it is as widely drawn upon and used as possible. But it comes with ethical dilemmas at many levels, including in relation to equity in which researchers contribute and gain from data being shared, how these complex processes are perceived by individuals and institutions, and how the integrity of the initial research endeavour is maintained.

Contestations can occur between collaborators as explored in the paper by Heeney (2017) below. Bull et al (2015) suggest that, “An effective model of data sharing will be one in which considered judgments will need to be made about how best to achieve scientific progress, minimize risks of harm, promote fairness and reciprocity, and build and sustain trust.” Questions about the transparency of the research process and the appropriateness of data sharing policies when applied to qualitative research have prompted some of these reflections (Tsai et al., 2016).

Through their analysis of the views of research stakeholders in Kenya, Jao et al. (2015) found that ethical data sharing was underpinned by strong and trusting relationships which enabled the co-construction of notions of risks such as stigma, loss of privacy, autonomy and misuse of data. They suggest that building an engagement infrastructure within the research process not only assists scientists in negotiating ethical practice but also increases the likelihood of the take up, use or translation of research in policy and practice decision making. Given the importance of relationships to data analysis and sharing some of the community engagement resources in the section on Building relationships may also be useful as you think through how to analyse the data that you have collected.

USEFUL RESOURCES

Choosing questions, study designs & methods

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Building relationships

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Collecting data

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Analysis and data sharing

Two women analysing data