A stakeholder-driven agenda for advancing the science and practice of scale-up and spread in health
Bibliographic record
Abstract
BACKGROUND: Although significant advances have been made in implementation science, comparatively less attention has been paid to broader scale-up and spread of effective health programs at the regional, national, or international level. To address this gap in research, practice and policy attention, representatives from key stakeholder groups launched an initiative to identify gaps and stimulate additional interest and activity in scale-up and spread of effective health programs. We describe the background and motivation for this initiative and the content, process, and outcomes of two main phases comprising the core of the initiative: a state-of-the-art conference to develop recommendations for advancing scale-up and spread and a follow-up activity to operationalize and prioritize the recommendations. The conference was held in Washington, D.C. during July 2010 and attended by 100 representatives from research, practice, policy, public health, healthcare, and international health communities; the follow-up activity was conducted remotely the following year. DISCUSSION: Conference attendees identified and prioritized five recommendations (and corresponding sub-recommendations) for advancing scale-up and spread in health: increase awareness, facilitate information exchange, develop new methods, apply new approaches for evaluation, and expand capacity. In the follow-up activity, 'develop new methods' was rated as most important recommendation; expanding capacity was rated as least important, although differences were relatively minor. SUMMARY: Based on the results of these efforts, we discuss priority activities that are needed to advance research, practice and policy to accelerate the scale-up and spread of effective health programs.
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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 | 0.468 | 0.280 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.018 | 0.033 |
| Scholarly communication | 0.034 | 0.054 |
| Open science | 0.011 | 0.030 |
| Research integrity | 0.055 | 0.047 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".