Bibliographic record
Abstract
Coordinated care plans are public health hospital interventions that aim to reduce friction and system inefficiencies within healthcare systems through the improved communication between various providers within and beyond the hospital ecosystem. In Canada, a small fraction of the population is responsible for more than 2/3 of total healthcare costs, consisting mostly of disabled or elderly patients. Furthermore, such patients often have several detrimental social determinants of health, including low sociodemographic status, food insecurity, unreliable social nets, and thus impede their ability to receive care. As a result, many of these “high risk” patients face fragmented or inadequate care, which compounds to a proportionally high healthcare use. Several nations including Canada have attempted to implement Coordinated Care Plans (CCPs) as a means to support these vulnerable patients by providing improved healthcare system navigation, and mediating certain social determinants of health. This scoping review will examine the background of CCPs, including previous literature on coordinated care plan effectiveness, and a thorough analysis on previous coordinated care plan studies specific to Ontario (Health Links). Knowledge gaps will be identified. Finally, the rationale and future directions of this study will be provided.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".