Exploring how structural forms of power shape the training of intraprofessional collaboration between family physicians and specialty physicians in outpatient workplace settings
Bibliographic record
Abstract
INTRODUCTION: Intraprofessional collaboration between family physicians (FPs) and specialist physicians (SPs) is posited to improve patient outcomes but is hindered by power dynamics. Research informing intraprofessional training on hospital wards often conceptualizes power at an interactional level. However, less is known about how social structures make these power dynamics possible. This study explores how structural forms of power shape how FP and SP supervisors engage with and teach intraprofessional collaboration in outpatient settings and to what effect. METHODS: Using diabetes as a case study of intraprofessional collaboration, we conducted a discourse analysis of formal documents (written to guide how collaboration should be practiced) and interview transcripts with 15 FP and SP supervisors. Informed by governmentality and the sociology of professions, we analysed how discourses governing diabetes care shape FPs' and SPs' clinical and teaching behaviours, implications for jurisdictional boundaries and the nature of their collaborative relationships. RESULTS: Discourses of evidence-based medicine construct a hierarchical social structure in medicine that permeates how physicians engage with and teach intraprofessional collaboration. FPs and SPs enact and teach these hierarchical roles when collaborating in the referral-consultation process in ways that establish and reinforce jurisdictional boundaries. The interactions at the intersection of these boundaries foster a form of collaboration characterized by SPs surveilling and regulating FPs' practices. DISCUSSION: As currently constructed, intraprofessional collaboration in outpatient settings may be practiced and taught in ways that reinforce asymmetric power dynamics between FPs and SPs. Without awareness of this unintentional effect, educational attempts to advance this constructed notion of collaboration may ironically impede the achievement of collaborative ideals. Outlining the processes by which structural power permeates FPs' and SPs' collaborative behaviours opens space for educators to acknowledge and mitigate the effects of social structures on intraprofessional training in other clinical and educational contexts.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".