What is the role of randomised trials in implementation science?
Bibliographic record
Abstract
BACKGROUND: There is a consistent demand for implementation science to inform global efforts to close the gap between evidence and practice. Key evaluation questions for any given implementation strategy concern the assessment and understanding of effects. Randomised trials are generally accepted as offering the most trustworthy design for establishing effectiveness but may be underused in implementation science. MAIN BODY: There is a continuing debate about the primacy of the place of randomised trials in evaluating implementation strategies, especially given the evolution of more rigorous quasi-experimental designs. Further critiques of trials for implementation science highlight that they cannot provide 'real world' evidence, address urgent and important questions, explain complex interventions nor understand contextual influences. We respond to these critiques of trials and highlight opportunities to enhance their timeliness and relevance through innovative designs, embedding within large-scale improvement programmes and harnessing routine data. Our suggestions for optimising the conditions for randomised trials of implementation strategies include strengthening partnerships with policy-makers and clinical leaders to realise the long-term value of rigorous evaluation and accelerating ethical approvals and decluttering governance procedures for lower risk studies. CONCLUSION: Policy-makers and researchers should avoid prematurely discarding trial designs when evaluating implementation strategies and work to enhance the conditions for their conduct.
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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 | 0.877 | 0.947 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.026 | 0.012 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.007 | 0.095 |
| Scholarly communication | 0.045 | 0.062 |
| Open science | 0.017 | 0.015 |
| Research integrity | 0.045 | 0.045 |
| Insufficient payload (model declined to judge) | 0.012 | 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; the direct Gemma label and the distilled Codex classifier 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".