Adaptation of public health initiatives: expert views on current guidance and opportunities to advance their application and benefit
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
While there is some guidance to support the adaptation of evidence-based public health interventions, little is known about adaptation in practice and how to best support public health practitioners in its operationalization. This qualitative study was undertaken with researchers, methodologists, policy makers and practitioners representing public health expert organizations and universities internationally to explore their views on available adaptation frameworks, elicit potential improvements to such guidance, and identify opportunities to improve implementation of public health initiatives. Participants attended a face to face workshop in Newcastle, Australia in October 2018 where World Café and focus group discussions using Appreciative Inquiry were undertaken. A number of limitations with current guidance were reported, including a lack of detail on 'how' to adapt, limited information on adaptation of implementation strategies and a number of structural issues related to the wording and ordering of elements within frameworks. A number of opportunities to advance the field was identified. Finally, a list of overarching principles that could be applied together with existing frameworks was generated and suggested to provide a practical way of supporting adaptation decisions in practice.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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