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Record W4396529068 · doi:10.3310/nihropenres.1115226.1

Programme Grants for Applied Research (PGfAR) logic model

2024· report· en· W4396529068 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
FundersProgramme Grants for Applied ResearchNational Institute for Health and Care ResearchMcMaster University
KeywordsComputer science

Abstract

fetched live from OpenAlex

Programme Grants for Applied Research (PGfAR) Logic Model Katie Cook1 (corresponding author), Rajinder Flora1, Joanna Cole-Hamilton2, Marian Knight3 1 PGfAR programme team, National Institute for Health and Care Research, LGC Ltd 2 Monitoring, Evaluation and Learning team, National Institute for Health and Care Research, LGC Ltd 3 University of Oxford, National Institute for Health and Care Research Director of Programme Grants for Applied Research The National Institute of Health and Care Research (NIHR) funds, enables and delivers world-leading health and social care research that improves people’s health and wellbeing, and promotes economic growth. Programme Grants for Applied Research (PGfAR) is an NIHR programme that funds collaborative, multidisciplinary research in an area of priority or need for the NHS, public health or the social care sector, with a particular emphasis on health and social care areas that cause significant burden. PGfAR aims to deliver research findings that will lead to clear and identifiable patient, service user or carer benefits, typically through promotion of health and wellbeing, prevention of ill health, and optimal care and disease management (including safety and quality). It funds collaborative, multidisciplinary research in areas of priority or need for the NHS, public health or the social care sector. Particular emphasis is placed on health and social care areas that cause significant burden where other research funders may not be focused, or where insufficient funding is available, with the aim of solving these health and social care challenges. A logic model is a useful tool that can be used for planning, implementation and evaluation of a programme. It is a simple way to summarise the core elements of a funding programme and visually represent the theory (i.e. ‘how it’s supposed to work’) of how a programme intends to bring about the desired benefits and changes. NIHR logic models comprise the funding programme resources (or ‘inputs’) and activities that NIHR undertakes, which then produce outputs (or direct results). These outputs accrue incrementally over time and, alongside engagement activities, lead to the anticipated short-term outcomes, and eventually long-term outcomes and impacts. A logic model was created to visually represent PGfAR. It sets out the essential elements of and pathway to impact for the NIHR funding programme Programme Grants for Applied Research. It was created for the Department of Health and Social Care (DHSC) by the PGfAR programme team in collaboration with the Monitoring, Evaluation and Learning team at the NIHR Central Commissioning Facility (NIHR CCF) with guidance and input from the PGfAR steering committee. The logic model is described below. Inputs The first component of the logic model focuses on inputs, the resources which are put into the programme in order to undertake the activities which produce the outputs. The inputs are: NIHR funding: PGfAR awards Pre-programme Programme Development Grants (PDGs) Post award PDGs Stakeholders: Applicants Host institutions and their infrastructure to support awards NHS, public health organisations and care organisations Public, patients, service users and carers Collaborations of appropriate applied health and social care (H&SC) practitioners and academics (including applied methodological experts) NIHR resources: Commissioning centre staff DHSC staff Multidisciplinary funding committee members Research management systems and processes NIHR infrastructure: For example, Centre for Engagement and Dissemination (CED), Research Support Service (RSS), Research Delivery Network (RDN) NIHR policy to guide applicants and award holders (for example, equality, diversity and inclusion (EDI), UK Standards for Public Involvement and others). Activities The above inputs feed into the second component of the logic model which consists of the activities conducted by the award holders and NIHR: NIHR activities: Identification of evidence gaps & demand for research in health and social care Commissioning and awarding of funding Award monitoring Linkage and involvement with other parts of NIHR and wider system (for example with patient and public involvement and engagement (PPIE), Research Support Service (RSS) and so forth). Pre-programme grant: PDG Stream A Researchers conduct preparatory research to reduce uncertainties & strengthen future PGfAR applications. PGfAR: Research delivery in line with agreed research plan Knowledge creation and dissemination Research translation Patient and public involvement and engagement (PPIE) throughout the project. Post-award Programme Grant (PG): PDG Stream B In parallel with the main award, to conduct additional linked research of strategic importance to the NIHR. Post-award Programme Grant (PG): PDG Stream B follow-on In the final stages of main award research to develop, analyse, and disseminate the programme or its outcomes further, in order to enable significant additional benefit to be realised for the NHS, public health, social care, patients, service users, carers or the wider public. Capacity and network building: Funding for targeted and equitable academic capacity development and training across the full career spectrum and specifically in methodological and underrepresented disciplines and professions NIHR Academy training programme uptake Establishing and developing collaborations. Outputs The outputs expected to result from the inputs and activities are detailed below. Outputs from Pre-programme grant: PDG Stream A Key development issues are addressed Apply for PGfAR award (where appropriate). Outputs from PGfAR awards: Portfolio of published generalisable applied health and care research Research evidence tailored to key audiences and suitable communications are produced and disseminated Research findings are cascaded to a range of audiences including decision-makers in suitable formats Award-holders apply, as appropriate, for follow-on funding. Outputs from follow-on PDG B awards: Outputs are ready for adoption Enhanced evidence base for key stakeholders. Research capacity building outputs: Strengthened pool of researchers and research leaders, and strengthened research institutions Career progression across the full career spectrum and in underrepresented disciplines and professions Additional funding is leveraged for follow-on research Lasting partnerships are created between researchers, health and care practitioners, service managers, patients, service users, carers and policy-makers. Short-Term Outcomes The inputs, activities and outputs are expected to create changes in the short and long term. The inputs, activities and outputs are within the control of PGfAR, whereas the short-term outcomes these create are within the programme’s sphere of influence rather than control, and the long-term impacts are outside the sphere of direct control or influence as there are many other factors which contribute to them. With that in mind, as a result of all the inputs, activities and outputs, we might expect the following outcomes in the short-term of nought to ten years. Increased knowledge base & scientific advancement: Improved evidence base on health and social care areas of significant burden. Methodological advancements Strong health and care research organisations are focused on the needs of the NHS, social care, public health, patients, service users, carers and the wider public. Research translation and adoption: New or updated health and care policies are shaped by research evidence. Products are sustained and maintained in service Changes and additions to guidance for practitioners, for example NICE, or Royal Colleges Outputs are adopted into use in the studied setting and other settings. Increased research capacity and enhanced networks: Empowerment for patients, service users and carers Stronger collaborations between stakeholders Strengthened capacity of NHS to manage research. Long-term outcomes / impacts Long term outcomes are those which might be seen from ten years onwards. They are changes which the programme hopes to contribute towards and will be influenced by multiple other factors. Health and Care Benefits: Identified evidence gaps and areas of significant burden are more effectively and efficiently addressed Improved health and care practice and delivery Benefits to patients, service users, carers, the public and society through improved health outcomes and improved wellbeing Reduce inequalities in health and care Reduction of disease progression / prevalence, including through a better informed patient, service user, carer and public base. Economic benefits: Value for money or efficiency gains within health and social care. Economic growth in the relevant industrial sectors and the wider economy. Assumptions For PGfAR to be delivered and achieve its outcomes through the theory described above, it is assumed that the following remains true through the course of the programme: Research evidence is translated for a range of audiences Decision-makers make evidence-informed decisions and adopt evidence into policy, service and practice Long-term outcomes and impacts are outside the direct control of the programme and many other factors will contribute to them, yet PGfAR hopes to achieve net health benefits, and efficiency through uptake of evidence and adoption of findings Public engagement, patient empowerment, and reducing inequalities are key themes that thread across the logic model. Research inputs and analysis of a problem should reflect an understanding of communities in the UK and their needs. Outputs should reflect the demographics the health and care system serves The flow across the logic model is not expected to be linear, and links can be multidimensional. Contributions and Acknowledgements This is an updated version of the PGfAR logic model. The original version was created by Katie Cook, Rajinder Flora, Shaun McMaster, and Elaine Hay, with support, insight and feedback throughout the process from members of the NIHR Programme Grants for Applied Research Committee and Laura Mason (NIHR Research for Social Care), Mark Taylor and Claire Vaughan (NIHR), and Britta Wyatt (Oxentia). The original version was published on 9 December 2022. This version was updated by the authors listed above. Competing interests This work has been undertaken as part of the delivery of the National Institute for Health and Care Research (NIHR), which is funded by the Department of Health and Social Care. All authors of this document have contributed to it as part of work paid for by the NIHR. No competing interests were disclosed. Keywords Logic model; theory of change; programme theory; programme grants; impact; outcomes; evaluation; applied research

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0020.003
Scholarly communication0.0070.010
Open science0.0030.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0740.018

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 head0.820
GPT teacher head0.641
Teacher spread0.179 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Published2024
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