Are they the same? Disentangling the concepts of implementation science research and population scale-up
Bibliographic record
Abstract
A new discipline, implementation science, has emerged in recent years. This has resulted in confusion between what 'implementation science' is and how it differs from real-world scale-up of health interventions. While there is considerable overlap, in this perspective, we seek to highlight some of the differences between these two concepts in relation to their origin, drivers, research methods and implications for population impact and practice. We recognise that implementation science generates new information on optimal methods and strategies to facilitate the uptake of evidence-based practices. This new knowledge can be used as part of any scaling-up endeavour. However, real-world scale-up is influenced to a much greater extent by political and strategic needs and key actors and generally requires the support of governments or large agencies that can fund population-level scale-up. Furthermore, scale-up often occurs in the absence of any evidence of effectiveness. Therefore, while implementation science and scale-up both ultimately aim to facilitate the uptake of interventions to improve population health, their immediate intentions differ, and these distinctions are worth highlighting for policymakers and researchers.
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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 | 0.583 | 0.618 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.005 | 0.088 |
| Scholarly communication | 0.035 | 0.060 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".