An Interprofessional Collaborative Practice Model: Primary-Care Clinical Associates at the Family Practice Setting
Bibliographic record
Abstract
A persistent physician shortage challenges the ability of our healthcare system to meet the growing health needs of our aging population. Health system redesign must maximize the efficient use of available human resources. The Alberta Westview Primary Care Network (WPCN) introduced the Primary Care Clinical Associate (CA) program in 2005. This interprofessional collaborative practice model recruits nonphysician health professionals from various disciplines as autonomous, independent practitioners. They are associates of the family physician and use their full scope of practice to jointly care for a panel of patients in family practice settings. Three years after its inception, the CA program has grown steadily from two initial participating family practices to its current implementation in six of seven WPCN clinics. The present direction of Canadian primary healthcare reform is towards team approaches. Accordingly, the CA program has wide applicability provincially across Canada. The objective of this article is to describe in detail the design of the WPCN CA program including its conceptual framework and operational strategies and to share program implementation learning. This knowledge transfer will enable replication of the WPCN CA model, where appropriate, in other jurisdictions.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".