Developing Tools for the Assessment of Primary Care Maturity and Health System Resource Allocation in Ontario, Canada
Bibliographic record
Abstract
Context The Frontenac, Lennox, and Addington Ontario Health Team (FLA OHT) is collaborating with regional primary care organizations to develop a roadmap for achieving a person-centered Health Home for all, including a gap analysis of the current primary care landscape. Objective Develop a roadmap for scaling the Health Home framework, identify co-design opportunities in program delivery, capture data for maturity and gap analyses, and support primary care organizations in achieving the Health Home vision centered around the Quintuple Aim. Additionally, develop a maturity model and resource allocation models for allied health professionals based on physician panel management and population health needs to enhance capacity and address unattached patients. Study Design and Analysis The current state assessment tool, co-designed with primary care partners, describes primary care organizations through facilitated interviews and surveys. Data from various practice types will answer research questions on team composition’s impact on capacity and access, and readiness for regional program planning. Setting or Dataset Qualitative and quantitative data on primary care organizations from the FLA region, including staffing counts. Population Studied Data from diverse primary care practice types in the FLA OHT, focusing on team composition and team maturity. Intervention/Instrument The current state assessment results will inform resource allocation modeling to better match resources and service/program planning with population health needs, adding capacity to attach more patients to primary care. Outcomes Measured Self-assessed Health Home maturity according to the current state assessment’s maturity matrix and allied staff resource ratios for given panel sizes/patient populations. Results Different primary care models are in varying maturity states. The current state assessment can be used longitudinally to maintain a database of primary care resources. The resource allocation model guides the additional resources required for attaching patients without overwhelming primary care physicians. Conclusions The current state assessment tool is valuable for primary care organizations in the FLA OHT, serving as a useful planning tool for ongoing resource mapping in regional health system planning. The resource allocation modeling helps guide the composition of expanded and potentially new teams based on population health needs.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".