Toward consensus on self-management support: the international chronic condition self-management support framework
Bibliographic record
Abstract
Self-management support (SMS) initiatives have been hampered by insufficient attention to underserved and disadvantaged populations, a lack of integration between health, personal and social domains, over emphasis on individual responsibility and insufficient attention to ethical issues. This paper describes a SMS framework that provides guidance in developing comprehensive and coordinated approaches to SMS that may address these gaps and provides direction for decision makers in developing and implementing SMS initiatives in key areas at local levels. The framework was developed by researchers, policy-makers, practitioners and consumers from 5 English-speaking countries and reviewed by 203 individuals in 16 countries using an e-survey process. While developments in SMS will inevitably reflect local and regional contexts and needs, the strategic framework provides an emerging consensus on how we need to move SMS conceptualization, planning and development forward. The framework provides definitions of self-management (SM) and SMS, a collective vision, eight guiding principles and seven strategic directions. The framework combines important and relevant SM issues into a strategic document that provides potential value to the SMS field by helping decision-makers plan SMS initiatives that reflect local and regional needs and by catalyzing and expanding our thinking about the SMS field in relation to system thinking; shared responsibility; health equity and ethical issues. The framework was developed with the understanding that our knowledge and experience of SMS is continually evolving and that it should be modified and adapted as more evidence is available, and approaches in SMS advance.
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 | 0.082 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".