Conducting retrospective impact analysis to inform a medical research charity’s funding strategies: the case of Asthma UK
Bibliographic record
Abstract
BACKGROUND: Debate is intensifying about how to assess the full range of impacts from medical research. Complexity increases when assessing the diverse funding streams of funders such as Asthma UK, a charitable patient organisation supporting medical research to benefit people with asthma. This paper aims to describe the various impacts identified from a range of Asthma UK research, and explore how Asthma UK utilised the characteristics of successful funding approaches to inform future research strategies. METHODS: We adapted the Payback Framework, using it both in a survey and to help structure interviews, documentary analysis, and case studies. We sent surveys to 153 lead researchers of projects, plus 10 past research fellows, and also conducted 14 detailed case studies. These covered nine projects and two fellowships, in addition to the innovative case studies on the professorial chairs (funded since 1988) and the MRC-Asthma UK Centre in Allergic Mechanisms of Asthma (the 'Centre') which together facilitated a comprehensive analysis of the whole funding portfolio. We organised each case study to capture whatever academic and wider societal impacts (or payback) might have arisen given the diverse timescales, size of funding involved, and extent to which Asthma UK funding contributed to the impacts. RESULTS: Projects recorded an average of four peer-reviewed journal articles. Together the chairs reported over 500 papers. All streams of funding attracted follow-on funding. Each of the various categories of societal impacts arose from only a minority of individual projects and fellowships. Some of the research portfolio is influencing asthma-related clinical guidelines, and some contributing to product development. The latter includes potentially major breakthroughs in asthma therapies (in immunotherapy, and new inhaled drugs) trialled by university spin-out companies. Such research-informed guidelines and medicines can, in turn, contribute to health improvements. The role of the chairs and the pioneering collaborative Centre is shown as being particularly important. CONCLUSIONS: We systematically demonstrate that all types of Asthma UK's research funding assessed are making impacts at different levels, but the main societal impacts from projects and fellowships come from a minority of those funded. Asthma UK used the study's findings, especially in relation to the Centre, to inform research funding strategies to promote the achievement of impact.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.278 | 0.380 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".