Family physicians’ power and team-based care: Lessons from a 60-year-old primary care clinic
Bibliographic record
Abstract
The article focuses on the tension between team-based care approaches that emphasize interprofessional collaboration and existing power imbalances between family physicians and other health care providers. It contributes to the literature on the implementation of team-based care models in primary care clinics by adopting a governance perspective, often overlooked in these transitions. While existing research has acknowledged power imbalances between family physicians and other health care providers, it has paid less attention to how governance mechanisms may shape these dynamics. Through an in-depth case study of a 60-year-old Canadian primary care co-operative, we address this gap and explore how these power issues play out in a context of collaborative governance and shared decision-making. The methodology is qualitative, relying mainly on data from 42 interviews, as well as on observations and document analysis. On the one hand, the research reveals that some governance mechanisms play an important role in team-based primary care settings, helping attenuate the tension and facilitating collaboration between providers. On the other hand, it shows that, even in a long-standing team-based care model promoting equality between health care professionals and between providers and patients, power imbalances persist. The research illustrates the cultural anchorage of medical domination, highlighting (i) the importance of looking at one organisation's informal norms and cultural context when implementing team-based approaches to care, as well as (ii) the critical need for interprofessional education to actively engage with and address the underlying power dynamics that exist within health care settings.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| 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".