Developing a Value-Based Framework for the Evaluation of Interprofessional Primary Care Teams in Ontario
Bibliographic record
Abstract
Context Ontario’s primary care system faces escalating access and workforce challenges, with over 2.2 million residents lacking attachment to a regular provider. Interprofessional primary care teams (IPCTs) – including Family Health Teams, Community Health Centres, and Nurse Practitioner-Led Clinics – are central to improving access, coordination, and equity. However, existing frameworks often focus on physician-led care and lack applicability to team-based models in Ontario. Objective To develop a comprehensive, evidence-informed framework for evaluating how IPCTs generate value within Ontario’s health system. Study Design and Analysis This multimethod approach included a systematic review and qualitative consultation. Thirteen national and international frameworks were analyzed to extract key concepts related to structure, process, and outcomes. Stakeholder consultations with team members, policymakers, and system leaders refined the model’s clarity, relevance, and applicability. Setting or Dataset Ontario’s interprofessional primary care sector, including relevant national and international frameworks for primary care quality, team functioning, and value-based care. Population Studied Thirteen frameworks were reviewed. Consultations included primary care team members, professional associations, health system leaders, and policymakers across Ontario. Intervention/Instrument A structured framework defining external enablers, organizational inputs, care delivery outputs, and Quintuple Aim outcomes. Outcome Measures Identification of core capacities (e.g., governance, HHR, infrastructure), service outputs (e.g., access, continuity, comprehensiveness), and outcome domains (e.g., patient experience, population health, equity). Results The final value framework links enablers (e.g., funding, policy, partnerships) and inputs (e.g., governance, HR capacity, infrastructure, data systems) to outputs such as timely access, care coordination, and team-based relationships. Outcomes align with the Quintuple Aim: improved patient experience, population health, equity, team well-being, and system efficiency. Conclusions This model offers a practical framework for evaluating and improving IPCTs. It supports performance measurement, system design, and accountability aligned with what matters to patients, providers, and communities.
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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 | 0.133 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| 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".