Continuing efforts to integrate care can benefit from cross-jurisdictional comparisons
Bibliographic record
Abstract
Despite its theoretical appeal, integrated care remains a new frontier for health and social care systems in many countries. A key motivation for increasing the integration of a range of health care services with socialor community-based services is the imperative to improve the patient experience, particularly for individuals with ongoing multiple and complex health care and functional needs. To meet these needs they receive care from many different providers and often they feel they are bouncing around in an uncoordinated system that requires them to repeatedly tell their stories but that fails to provide a clearly articulated coordinated plan of care to help them manage their conditions. A key to improving the patient experience, care coordination and outcomes, is the need to address failures associated with low-fidelity of information sharing among providers, as patients transition from one provider to another. Failure to share information on treatment goals and therapies almost inevitably results in an incoherent treatment plan that often is duplicative or self-defeating and that in some cases causes more harm than good. Along with improving the patient experience, integrated care can increase the system-level efficiency of treatment and lower costs. Bringing multiple services into a coordinated network where information is centrally held and shared supports coordinated scheduling, shared access to essential information and reduction in duplication of diagnostic tests. In theory integrated care can improve patient experience and outcomes while reducing costs.
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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 | 0.097 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".