Intercountry Consensus Building: Lessons From Developing a Chronic-Conditions Self-Management Support Framework
Bibliographic record
Abstract
Self-management support initiatives that aim to improve the self-care of chronic conditions are considered a key part of a health promotion strategy for addressing the impacts of long-term illness. Given the growth of these activities and still evolving evidence base, thoughtful intercountry collaborations with subject matter experts can be an effective way to expedite building self-management support capacity, promoting the advancement of evidence, and developing effective policies and programs. The challenge is to find an effective consensus building process that promotes linkages between researchers and health promotion decisions makers across vast geographical boundaries and limited resources. This paper describes the international, multistage, face-to-face, and online process that was used for developing an international framework for self-management support by researchers, educators, health care providers, policy makers, program managers/directors, program planners, consultants, patient group representatives, and consumers in 16 countries. We reflect on key lessons from this international initiative and discuss how this type of process may be useful for other health promotion groups trying to exchange knowledge and build consensus on how to move a field of research, policy, and/or practice forward, and advance the evidence-base of practice and the relevance of research.
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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 | 0.202 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".