A stakeholder-driven agenda for advancing the science and practice of scale-up and spread in health
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
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.
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 | 0.040 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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