The Evolution of Family Physician Leadership in Health System Change
Bibliographic record
Abstract
Context: Several months before the arrival of COVID-19, the province of Ontario rolled out a new structure of integrated health care delivery known as Ontario Health Teams (OHTs). Key to the development of OHTs was the intentional engagement of family physicians (FPs). Objective: This ongoing study initially explored the role of FP leaders and their role as champions in engaging community-based FPs in OHTs. After 3 years of study, we can now describe the evolution of the FP leaders’ roles and activities. Study Design and Analysis: Descriptive qualitative study using individual interviews. An iterative and interpretive process was conducted with individual and team analysis to identify overarching themes. Setting: Regional health care organizations in Ontario, Canada. Population Studied: A purposive sample of 35 primary care leaders (48 interviews in total) engaged in health system change from across Ontario, from January 2020 – April 2022. Results: Over the last 3 years, an evolution has been occurring as FP leaders transition from being champions who push out ideas to leaders who co-create and enact ideas. Their initial motivation to serve as leaders was to advocate for their patients and the discipline of Family Medicine while being committed to making significant changes in how health care is provided. While these basic principles remain, the COVID-19 pandemic required that they shift their priorities and activities. This included planning the procurement of PPE and organizing health human resources for assessment and vaccination centres. In addition, many FP leaders were called upon to contribute to pandemic response planning organizations, locally and provincially. Collectively, they brought a strong voice for primary care to these tables, often serving as brokers and bridge-builders across multiple sectors. For some, responsibility of the work and volume became exhausting, raising concerns about potential burnout. The recognition of the need to develop sustainable infrastructure to enlarge the pool of FP leaders and to maintain current leaders was strongly emphasized. Conclusion: These findings provide a unique perspective on the evolution of FP leadership in health system change and the contextual factors that are influencing this transition.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".