Adapting Our SCOPE: Lessons Learned from Spreading and Scaling Efforts to Integrate Care
Bibliographic record
Abstract
SCOPE (Seamless Care Optimizing the Patient Experience) launched in 2012 to support primary care in downtown Toronto with live navigation and rapid access to acute and community care resources for primary care providers (PCPs) and their patients. Ten years later, over 1,800 PCPs across Ontario have signed up for SCOPE and over 48,000 interactions in the form of e-mail, fax, phone and secure messaging have been conducted. Case examples illustrate the ways in which SCOPE has been adapted across a range of Ontario Health Teams, including under-resourced, small urban and rural sites. Primary care engagement, change management strategies and flexibility to meet the individual needs of each site have been key factors in the successful spread and scale of SCOPE's services.
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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 | 0.054 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".