Governance in the transformational journey toward integrated healthcare: The case of Ontario
Bibliographic record
Abstract
Ontario, the most populous province and the main driver of economic growth in Canada, has been plagued by rising healthcare costs and declining health quality. Firmly believing that care integration is integral to health quality and efficiency of the healthcare system, the Ontario government has embarked on its journey of healthcare system transformation. The case, by taking a snapshot of its transition from the Local Health Integration Network (LHIN) model to the Ontario Health Team model, offers insights into the governance of the LHIN model as well as the initiative of integrating a myriad of healthcare information systems to support care integration. After 12 years’ controversial operation of LHINs, the government dissolved the organization and announced its plan to switch to the Ontario Health Team model. Under the new model, Ontario Health Teams would be responsible for providing a full and coordinated spectrum of care for patients, especially those with complex care needs. Would this round of transformation and emphatic focus on digital health lead Ontario one step closer to integrated care that Ontarians have been chasing for so long?
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.029 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".