Re-Framing the Knowledge to Action Challenge Through NIHR Knowledge Mobilisation Research Fellows Comment on "CIHR Health System Impact Fellows: Reflections on ‘Driving Change’ Within the Health System"
Bibliographic record
Abstract
The ambition of the Canadian Institutes for Health Research Health System Impact (HSI) Fellowship initiative to modernise the health system is impressive. Embedded researchers who work between academia and non-academic settings offer an opportunity to reframe the problem of evidence uptake as a product of a gap between those who produce knowledge and those who use it. As such, there has been an increasing interest in the potential of people in embedded research roles to work with stakeholders in the co-production of knowledge to address service challenges. In this commentary, we draw on research and experiential evidence of an embedded researcher initiative, which has similar intentions to the HSI Fellowships programme: the National Institute for Health Research (NIHR) Knowledge Mobilisation Research Fellowship (KMRF) scheme. We outline the similarities and differences between the two schemes, and then consider the work, characteristics and skills, and organisational arrangements evident in operationalising these types of roles.
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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 | 0.022 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.094 | 0.080 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".