Are they the same? Disentangling the concepts of implementation science research and population scale-up
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it