The Interplay of Inter- and Intraprofessional Boundary Work in Multidisciplinary Teams
Bibliographic record
Abstract
The challenges of managing interprofessional boundaries within multidisciplinary teams are well known. However, the role of intraprofessional relations in influencing the dynamics of interprofessional collaboration remain underexplored. Our qualitative study offers a fine-grained analysis of the interplay between inter- and intraprofessional boundary work among three professional groups in a multidisciplinary team over a period of two years. Our contribution to the literature is threefold. First, we identify various forms of “competitive” and “collaborative” boundary work that may occur simultaneously at both inter- and intraprofessional levels. Second, we reveal the dynamic interplay between inter- and intraprofessional boundary negotiations over time. Third, we theorize relationships between the social position of professional groups, and the uses and consequences of competitive and collaborative boundary work tactics at intra- and interprofessional levels. Specifically, we show how intraprofessional conflict within high-status groups may affect interprofessional dynamics, we reveal how intraprofessional and interprofessional boundaries may be mobilized positively to support collaborative relations, and we show how mobilization within lower-status groups around interprofessional boundary grievances can paradoxically lead to further marginalization.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.014 |
| 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".