Workshop report - International roundtable on the self-management support of chronic conditions
Bibliographic record
Abstract
An international roundtable on self-management support (SMS) for persons living with chronic conditions (CCs) was held in Vancouver, Canada, in June 2009. It brought together 23 leading researchers, policy makers, health care practitioners and consumers from Canada, Australia, New Zealand, the United Kingdom and the United States. It also provided a forum for critically reflecting on SMS approaches and for building consensus on how to move forward in the self-management field. The deliberations resulted in a draft international framework that identifies key definitions, principles and strategic directions and also outlines sample strategies to guide those working to develop SMS capacities at the local, regional or national level. The framework is a mechanism for knowledge exchange that will hopefully act as a catalyst to shift SMS-related policy, practice and research directions to better serve the needs of all CC populations. More than 400 multi-level stakeholders in the Canadian and international community have been invited to review the framework using an e-consultation process. The final framework is scheduled for release in the late fall of 2011.
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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".