Optimizing Interprofessional Resources in Primary Care through a Primary Care Neighbourhood Network
Bibliographic record
Abstract
Join us for an engaging networking session where we explore the innovative Primary Care Neighbourhood Network (PCNN) model being implemented in the Peel region of Ontario. This session is your chance to connect with diverse health leaders and stakeholders, share your expertise, and discuss the future of interprofessional collaboration in primary care and share any insights and lessons learned from other jurisdictions or partners. Designed to increase access to primary care for vulnerable and marginalized populations, the PCNN brings together 0 partner organizations - ranging from community health centres to mental health associations - in a collaborative effort to attach 0,000 patients within the next year. Together, we'll dive into the innovative features of this model, including centralized intake, resource sharing, and a focus on population health. We'll also use patient/client personas to explore how this innovative model can be adapted to other regions and how we can build trust and overcome skepticism in resource-sharing efforts across partners. Don't miss this opportunity to expand your network and exchange ideas with global peers on solutions to some of primary care's most pressing challenges. Who should attend: Health leaders, primary care providers, allied health professionals, patients and caregivers, policy makers, researchers, public health experts, IT specialists, and community advocates.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".