Priority-setting in public health research funding organisations: an exploratory qualitative study among five high-profile funders
Bibliographic record
Abstract
BACKGROUND: Priority-driven funding streams for population and public health are an important part of the health research landscape and contribute to orienting future scholarship in the field. While research priorities are often made public through targeted calls for research, less is known about how research funding organisations arrive at said priorities. Our objective was to explore how public health research funding organisations develop priorities for strategic extramural research funding programmes. METHODS: Content analysis of published academic and grey literature and key informant interviews for five public and private funders of public health research in the United Kingdom, Australia, the United States and France were performed. RESULTS: We found important distinctions in how funding organisations processed potential research priorities through four non-sequential phases, namely idea generation, idea analysis, idea socialisation and idea selection. Funders generally involved the public health research community and public health decision-makers in idea generation and socialisation, but other groups of stakeholders (e.g. the public, advocacy organisations) were not as frequently included. CONCLUSIONS: Priority-setting for strategic funding programmes in public health research involves consultation mainly with researchers in the early phase of the process. There is an opportunity for greater breadth of participation and more transparency in priority-setting mechanisms for strategic funding programmes in population and public health research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.188 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; both teacher heads 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".