Governance of health research funding institutions: an integrated conceptual framework and actionable functions of governance
Bibliographic record
Abstract
BACKGROUND: Health research has scientific, social and political impacts. To achieve such impacts, several institutions need to participate; however, health research funding institutions are seldom nominated in the literature as essential players. The attention they have received has so far focused mainly on their role in knowledge translation, informing policy-making and the need to organise health research systems. In this article, we will focus solely on the governance of national health research funding institutions. Our objectives are to identify the main functions of governance for such institutions and actionable governance functions. This research should be useful in several ways, including in highlighting, tracking and measuring the governance trends in a given funding institution, and to forestall low-level governance. METHODS: First, we reviewed existing frameworks in the grey literature, selecting seven relevant documents. Second, we developed an integrated framework for health research funding institution governance and management. Third, we extracted actionable information for governance by selecting a mix of North American, European and Asian institutions that had documentation available in English (e.g. annual report, legal status, strategy). RESULTS: The framework contains 13 functions - 5 dedicated to governance (intelligence acquisition, resourcing and instrumentation, relationships management, accountability and performance, and strategy formulation), 3 dedicated to management (priority-setting, financing and knowledge transfer), and 5 dedicated to transversal logics that apply to both governance and management (ethics, transparency, capacity reinforcement, monitoring and evaluation, and public engagement). CONCLUSIONS: Herein, we provide a conceptual contribution for scholars in the field of governance and health research as well as a practical contribution, with actionable functions for high-level managers in charge of the governance of health research funding institutions.
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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 | 0.055 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".