Expectations of the Ontario Healthcare System following the Implementation of Ontario Health Teams
Bibliographic record
Abstract
With the arrival of Ontario Health Teams (OHTs), healthcare providers, clinicians and patients seek to witness the efficacy of an integrated care model. OHTs are built on the concept of healthcare integration, coordinated care, shared fiscal and clinical accountabilities between multiple healthcare service providers, as well as bridging the gaps between the clinical, social and health promotional aspects of care delivery. This meta-narrative review seeks to examine, compare and determine the efficacy of the integrated care model using cross-sectional studies from around the world to see how integrated care effects health related outcomes. The efficacy of the model will be determined by evaluating the abilities of other integrated care models to reduce healthcare expenditures, improve coordination of care between healthcare service providers, bolster patient satisfaction and health outcomes, minimise emergency and life-threatening cases, lower emergency hospital admission rates as well as provide a comprehensive set of healthcare services including biomedical, mental and social supports. For future applications, this study could be used as a guideline to highlight areas of improvement in integrated care models, as well as to evaluate benefits of existing models and determine best approaches forward.
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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".