Interprofessional Teams for Chronic Disease Management \n
Bibliographic record
Abstract
Research Question: \n \n“Is there reliable scientific evidence to support team-based management of chronic disease and, if so, given the NL context (in terms of geography, demography, fiscal resources and health system capacities) what is the most effective and efficient way to organize, implement, and sustain team-based care for individuals with diabetes and chronic obstructive pulmonary disease (COPD) so as to derive the best possible outcomes for patients, providers, and the health system?” \n \nFindings: \n \nAs we worked through the many challenges of synthesizing the evidence on team-based chronic disease management, the project team concluded, in consultation with our subject experts, that we are unable to answer this CHRSP question as formulated. As our report will show, the high-level research evidence on both the clinical and cost-effectiveness of team-based Chronic Disease Management is simply not available at this time. In short, the question itself is ahead of the published literature. We are hopeful that, as research in this subject area advances, we may be in a better position to provide some guidance on this question in future. \n \n
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.112 | 0.015 |
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".