As good as it gets? : strategies for improving chronic care management : proceedings of the 14th annual health policy conference of the Centre for Health Services and Policy Research, November 8, 2002
Bibliographic record
Abstract
CHSPR's 17th annual health policy conference — As Good as It Gets? Strategies for Improving Chronic Care Management—brought together researchers, clinicians and high-level public servants from B.C. and the United States to discuss the challenges and opportunities surrounding chronic disease management. While British Columbia is ahead of the rest of Canada in this area, we're still at the beginning of a long journey. There was a general consensus among conference participants that there is an emerging global epidemic of chronic illness, and that the current system, which is focused on acute conditions, is not dealing with it effectively. The importance of co-morbidity—multiple chronic conditions—in the management of chronic illness care and in health planning was also recognized. The 'chronic care model' was a key point of reference in most discussions. Several chronic disease management initiatives were described, both from B.C. and from other jurisdictions, along with various methods of measuring chronic illness care. And there was both optimism and apprehension about how we are doing in our efforts to improve the way we care for people with chronic conditions.
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 | 0.052 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.035 | 0.016 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.040 | 0.034 |
| Insufficient payload (model declined to judge) | 0.013 | 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".