Study of the Local Health Integration Network: impact of Ontario’s Regionalization Policy
Bibliographic record
Abstract
Purpose The purpose of this paper is to undertake an examination of the Local Health Integration Network (LHIN) Health Policy proposal. This policy established a decentralized approach to health system management in the province of Ontario, Canada by creating 14 crown agencies, LHINs. Design/methodology/approach This policy is examined against the five policy stages of the Stages Model: agenda setting, formulation, legitimation, implementation and evaluation. The examination was based on a review of grey literature, including key government reports and briefs. Findings This policy did not follow the Stages Model sequentially: the policy was implemented while it was still undergoing its legitimacy phase. Formal reviews were undertaken following implementation and found areas for improvement: poor integration amongst all the LHINs; poor patient navigation persists; LHINs lack the capacity and competency to engage in regional capacity planning; and planning and integration is not centered around patient needs. As a result, a decade after the introduction of LHINs, the Ontario HealthCare System has not achieved systems improvement when measured against accepted government indicators of performance. Originality/value This integration policy highlights the context and evolution of Ontario’s healthcare system governance in the past decade and contributes to the body of knowledge on the impact of regionalization on health systems and patient care.
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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 | 0.015 | 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 | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".