An environmental scan of Ontario Health Teams: a descriptive study
Bibliographic record
Abstract
BACKGROUND: Ontario Health Teams (OHTs) are an integrated care system introduced in Ontario, Canada in 2019 after the 14 Local Health Integrated Networks (LHINs) were dissolved. The objective of this study is to give an overview of the current state of the OHT model's implementation, and what priority populations and transitions of care models were identified by OHTs. METHODS: This scan involved a structured search for each approved OHT of publicly available resources with three main sources: the full application submitted by the OHT, the OHT website, and a Google search with the name of the OHT. RESULTS: As of July 23, 2021, there were 42 approved OHTs and nine transitions of care programs were identified across nine OHTs. Of the approved OHTs, 38 had identified ten distinct priority populations, and 34 reported partnerships with organizations. CONCLUSIONS: While the approved OHTs currently cover 86% of Ontario's population, not all OHTs are at the same stage of activity. Several areas for improvement were identified, including public engagement, reporting, and accountability. Moreover, OHTs' progress and outcomes should be measured in a standardized manner. These findings may be of interest to healthcare policy or decision-makers looking to implement similar integrated care systems and improve healthcare delivery in their jurisdictions.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.024 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".