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Record W2100689694 · doi:10.1186/1710-1492-9-17

Conducting retrospective impact analysis to inform a medical research charity’s funding strategies: the case of Asthma UK

2013· article· en· W2100689694 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
FundersAsthma and Lung UK
KeywordsAsthmaPortfolioGrant fundingPolitical scienceBusinessMedicinePublic relationsMedical educationFinancePublic administration

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.263
GPT teacher head0.552
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it