Bibliographic record
Abstract
As the health care system transforms to better meet the needs of their residents, the emergence and maturation of integrated care groups, such as Ontario Health Teams, are driving health system improvements. Over the past few years, the eHealth Centre of Excellence has supported over 32 Ontario Health Teams at various levels of maturity, with digital health supports that enable communication among clinicians, reduce administrative burden, improve access to care for patients and enable primary care efforts in population health management and provision of proactive care, for a sustainable health care system.This session will feature a number of digital solutions and supports that will help build capacity for integrated care delivery within communities. The congestive heart failure integrated care pathway will be used as one example as we delve into the exciting world of automated solutions, which include a family of that can streamline primary care workflows to care for patients at risk. Followed by EMR-integrated decision support tools that give clinicians quick and easy access to best practice information on heart failure at the point of care and a wide range of other enablers, including AI Scribes, eReferral, eConsult, and Online Appointment Booking, all of which support optimized processes to build capacity and strengthen primary care, ensuring that patients receive high-quality care, when and where they need it.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.346 | 0.112 |
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".