Scaling Innovation in a Publicly Funded System: A UK Pathway From Evidence to Adoption
Bibliographic record
Abstract
According to Manns et al. (2025), Canada struggles to turn good ideas into routine care because functions for evidence, funding, procurement and delivery are fragmented. In the UK, these functions are, in part, connected within a tax-funded service free at the point of use. This commentary maps the architecture, linking research translation, independent assessment, regulation, procurement, adoption support and data and explains how evidence moves into practice through principles aligned with the nonadoption, abandonment, scale-up, spread and sustainability framework, which addresses nonadoption, abandonment, the challenges of scale-up, spread and sustainability. Two worked examples, placental growth factor testing and stroke imaging artificial intelligence, show that national assessment, adoption support and procurement enabled rapid adoption at a national scale. Practical implications for Canada include a single repeatable pathway from promising evidence to routine use, conditional adoption with evidence generation, national frameworks that reduce transaction costs, investment in implementation capability and secure data environments for real-world evaluation.
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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 | 0.277 | 0.478 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.046 | 0.027 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".