{"id":25211,"date":"2020-11-23T23:30:40","date_gmt":"2020-11-23T23:30:40","guid":{"rendered":"https:\/\/technopolis-group.com\/?p=25211"},"modified":"2020-12-01T09:05:05","modified_gmt":"2020-12-01T09:05:05","slug":"lessons-learnt-from-the-iamhrf-global-study-on-mental-health-research-funding","status":"publish","type":"post","link":"https:\/\/technopolis-group.com\/fr\/lessons-learnt-from-the-iamhrf-global-study-on-mental-health-research-funding\/","title":{"rendered":"Lessons learnt from the IAMHRF Global Study on Mental Health Research Funding"},"content":{"rendered":"\n

Health research is a global undertaking with worldwide significance. Research funders need to understand the broader funding landscape to shape their research strategies and ensure the global research ecosystem functions efficiently. Yet, systematic analyses of research funding exist only for very few disciplines. We have supported the first quantitative analysis of mental health research funding on a global scale,[1]<\/a> working together with the International Alliance of Mental Health Research Funders (IAMHRF) and Digital Science and building on work by the charity MQ: Transforming mental health. <\/em><\/p>\n\n\n\n

Why analyse global research funding for a\nspecific field of research?<\/strong><\/h4>\n\n\n\n

By setting funding levels and designing\nfunding programmes, policy makers and funders directly influence the direction\nof research. Grants data can provide a detailed understanding of funding flows\nfor a particular area. This allows the detection of gaps in the funding\nlandscape and provides a robust platform from which to ask probing questions\nabout appropriate distribution of funds and lead conversations about\ncollaborations between funders. Measuring funding levels also plays an important\nrole in monitoring funding commitments for a particular area and in assessing\nreturns on investment. <\/p>\n\n\n\n

While data alone does not hold all the\nanswers, it is increasingly seen as an essential resource for developing and\ncoordinating funding strategies. For example, the IAMHRF Global Study on Mental\nHealth Research Funding points to stark inequalities, most notably across\nregions, that \u2013 even if not fully unexpected \u2013 gain significant weight when\nrigorously quantified and visualised. <\/p>\n\n\n\n

Where to start<\/strong><\/h4>\n\n\n\n

When\nanalysing the funding landscape, an important first step is to define the scope\nof the analysis and the questions stakeholders want to ask. Delineating the\nfunding of a specific field of research from the wider body of related research\nis an intricate task, and grey areas and uncertainties will likely remain. Building\non existing taxonomies and consulting with experts and stakeholders ensures\nbuy-in and alignment with important reference datasets such as burden of\ndisease statistics. Taxonomies can also be derived from data directly, for\nexample using tools that assess the similarity between different texts to\nderive clusters, or tools that are based on citation links of publications.<\/p>\n\n\n\n

In our\nparticular example, mental health research is challenging to define because it\nis closely related to neurological research. Yet, work by the charity MQ:\nTransforming mental health has demonstrated that it is possible to define\nusable inclusion and exclusion criteria as well as sub-fields for the field of mental\nhealth research.[2]<\/a> The\nIAMHRF Global Study on Mental Health Research Funding builds on this foundation.<\/p>\n\n\n\n

\"\"
Burden of disease for selected conditions compared to amount spent on research. Source: International Alliance of Mental Health Research Funders [1]<\/em><\/figcaption><\/figure><\/div>\n\n\n\n

What can funding data tell us?<\/strong><\/h4>\n\n\n\n

Research funders commonly hold a record\nof their funded projects and increasingly share information on their portfolio\npublicly. Available data points typically include a short description of the\nproject, information about the funding organisation, the hosting institution\nand the principal investigators, funding amounts, and the project timeframe, as\nwell as automated classification using one or several standardised systems such\nas Field of Research, the Health Research Classification System, or Medical Subject\nHeadings. Unfortunately, as found in the IAMHRF Global Study on Mental Health\nResearch Funding, publicly available funding data still has important gaps,\nincluding large parts of philanthropic and practically all industry funding.\nMore detailed data is often held by funders \u2013 but not usually shared outside\nthe organisation due to confidentiality. However, this could in principle be\nused for classification within the funders\u2019 protected environments. <\/p>\n\n\n\n

Project abstracts are usually sufficient\nto classify projects according to disease areas, which helps identify neglected\nareas. The IAMHRF Global Study on Mental Health Research Funding revealed that\nrelatively little research had addressed the issues of suicide and self-harm,\neating, conduct and personality disorders.<\/p>\n\n\n\n

Projects can also be mapped onto the\ntranslational research pathway, which gives insights into the level of funding\nprovided at different stages, from basic research, research into aetiology and\nprevention, to the development of treatments and health services research.\nGeographical data can be used to assess where funding flows, though information\nabout subcontracted institutions may be limited. Information on the research\nteams (e.g. discipline, demographics) is limited in public grants databases,\nbut could in principle be derived using bibliometric approaches. Likewise, details\non study participants (e.g. age, gender, race) are not readily available, but can\nto some degree be inferred from project abstracts. This was done in the IAMHRF\nGlobal Study on Mental Health Research Funding, showing that younger and older\nage groups receive smaller shares of funding.<\/p>\n\n\n\n

Manual classification vs. natural language\nprocessing<\/strong><\/h4>\n\n\n\n

Before the widespread use of machine\nlearning and natural language processing, landscape analyses were resource\nintensive. Potentially relevant grants were identified using funder profile,\nfunding stream, or keyword searches. Human coders then had to read titles and\nabstracts of each project to assign any classifications for analysis. This required\nworking closely with participating organisations and training a large network\nof coders in standardised coding rules. While it is helpful to deeply engage funders,\nthe associated costs can also present a barrier to participation and limit the coverage\nof the resulting dataset. <\/p>\n\n\n\n

Machine learning can identify and\nclassify relevant grants in an automated way, allowing large amounts of data to\nbe processed quickly. The accuracy of automated classification depends on many\nfactors, including the quality of the available data (i.e. how clearly does the\nproject description articulate the focus of the funded research) and the sophistication\nand training of the classification tools. It is important to note that manual\nclassification is not free from error, and ambiguities and grey areas mean that\ndifferent human coders may disagree on the coding for a particular grant. <\/p>\n\n\n\n

The IAMHRF Global Study on Mental Health\nResearch Funding fully relied on pre-existing automated classification tools\nimplemented on the Dimensions database and as well as bespoke tools made\navailable by MQ: Transforming mental health.2<\/sup> Testing small samples\nmanually showed that accuracy was acceptable for a first scoping study of this\nkind, although there is clearly room for improvement. A recent study has found\nautomated approaches can even outperform human coding.[3]<\/a><\/p>\n\n\n\n

Final thoughts<\/strong><\/h4>\n\n\n\n

Research funding analyses provide important insights that can help to define and coordinate funding strategies within particular fields of research. They are much facilitated by recent trends in data sharing and harmonisation, alongside advances in automated processing of text data, which have substantially dropped the cost. It is critical to involve stakeholders early in the process to ensure analyses are useful and to maximise data quality and completeness. The IAMHRF Global Study on Mental Health Research Funding has demonstrated that it is feasible and useful to gather data about mental health research funding on a global scale \u2013 a concerted effort by the global community of mental health research funders is now needed to build a longer-term solution for repeated and refined analyses.<\/p>\n\n\n\n

The IAMHRF Global Study on Mental Health Research Funding is available here<\/a>.<\/p>\n\n\n\n

\n\n\n\n

[1]<\/a> Woelbert E., Lundell-Smith, K., White, R. and Kemmer, D.(2020). Accounting for mental health research funding: developing a quantitative baseline of global investments. The Lancet Psychiatry. https:\/\/www.thelancet.com\/journals\/lanpsy\/article\/PIIS2215-0366(20)30469-7\/fulltext<\/a>

Woelbert, E., White, R., Lundell-Smith, K. Grant, J. and Kemmer, D. (2020). The Inequities of Mental Health Research. International Alliance of Mental Health Research Funders. https:\/\/digitalscience.figshare.com\/articles\/report\/The_Inequities_of_Mental_Health_Research_IAMHRF_\/13055897<\/a><\/p>\n\n\n\n

[2]<\/a> Woelbert, E., Kirtley, A., Balmer, N., & Dix, S. (2019). How\nmuch is spent on mental health research. Developing a system for categorising\ngrant funding in the UK. The Lancet Psychiatry. https:\/\/doi.org\/10.1016\/S2215-0366(19)30033-1<\/a>  <\/p>\n\n\n\n

[3]<\/a> Goh, Y.C., Cai, X.Q., Theseira, W. et al. Evaluating human versus\nmachine learning performance in classifying research abstracts. Scientometrics\n(2020). https:\/\/doi.org\/10.1007\/s11192-020-03614-2<\/a>\n<\/p>\n","protected":false},"excerpt":{"rendered":"

Health research is a global undertaking with worldwide significance. 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