Adaptation of public health initiatives: expert views on current guidance and opportunities to advance their application and benefit
Bibliographic record
Abstract
Abstract Background 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 study explored public health researchers’ and practitioners’ views on available adaptation frameworks, identified potential improvements to increase the application of such guidance and identified opportunities to advance the adaptation literature to improve implementation of public health initiatives and achieve public health outcomes. Methods This qualitative study was undertaken with researchers, methodologists, policy makers and practitioners representing various public health expert organizations and universities internationally. Participants attended a face to face workshop in Newcastle, Australia in October 2018 where structured facilitated focus group discussions and small group activities were undertaken. Two qualitative methods - World Café and Appreciative Inquiry - were used to explore participants’ experiences of and opinions about adaptation frameworks, and to elicit suggestions for practical, detailed solutions to address identified gaps. Results Participants reported a number of limitations with current guidance including a lack of detail on ‘how’ to adapt, limited information on adaptation of implementation strategies and a number of structural issues with the wording and recommended order of adaptation framework elements. Participants noted that the generation of overarching principles that could be applied together with existing frameworks and/or related resources provides a practical way of supporting adaptation decisions in practice. Finally, participants identified a number of opportunities to further advance the field, including the building of an empirical evidence base on adaptation, advancing adaptation methods and developing data collection systems to capture adaptations. Conclusions This study provides an overview of expert views regarding existing frameworks for adaptation and their ability to be applied in practice. It identified a number of limitations, and suggests overarching principles to be applied when adapting public health initiatives.
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.
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 | 0.333 | 0.381 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".