Primary care physician engagement in health systems transformation
Bibliographic record
Abstract
Physician engagement is critical to the success of primary care transformation, yet strategies to support meaningful engagement remain understudied. Despite existing research, gaps persist in understanding how physician engagement unfolds within system-level initiatives in primary care. This paper examines physician engagement through the development of the London Middlesex Primary Care Alliance (LMPCA), a regional initiative uniting primary care providers in Southwestern Ontario to advocate for system improvements and support health system transformation, including the Middlesex-London Ontario Health Team (ML-OHT). Rather than centering solely on physician perspectives, our study explores physician engagement as part of a broader collaborative effort involving healthcare administrators and support personnel. Data were collected through interviews (n = 13; including primary care physicians, healthcare administrators, and administrative support personnel), document analysis, and an environmental scan. Findings highlight the importance of grassroots leadership, governance structures, and system-level supports in driving physician engagement. The role of a primary care transformation lead emerged as a key facilitator, while lack of compensation for system-level work remained a barrier. This study provides insights into the formation of a sustainable, self-governing primary care organization and offers considerations for scaling engagement strategies while mitigating burnout and ensuring long-term participation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".