Professional Role Identity: At the Heart of Medical Collaboration Across Organisational Boundaries
Bibliographic record
Abstract
PURPOSE: The purpose of this paper was to help answer two persistent calls in the literature: the first asks to strengthen the understanding of medical collaboration across levels of healthcare delivery; the second one requests paying more attention to the individual experience of different forms of professional work. Accordingly, the study was guided by the following research question: How do family physicians and specialists working at different levels of healthcare delivery enact their professional identity when interacting in their situated clinical contexts? METHODOLOGY: This was a multiple interpretive case study in which, based on Giddens' ideas, professional identity was viewed as a dynamic structural element of social life recursively related to professionals' collaborative actions through sensemaking processes. The study involved 57 participants. Face-to-face individual semi-structured interviews and organizational documents were the main sources of data. Deductive-inductive thematic analysis was adopted as strategy for data analysis. FINDINGS: Three prevailing physicians' identity roles were elicited: medical expert, care coordinator, and team member. These professional identities, not mutually exclusive, were instantiated in three specific modalities of collaboration: quasi-inexistent, restrained, and extended. The entanglement of a particular identity role and a specific collaborative practice became meaningful through a complex net of organizational and institutional features, and patients' nosological profiles.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".