Leadership as boundary work in healthcare teams
Bibliographic record
Abstract
This paper proposes that boundary work is inherent to leadership practices in healthcare settings, and explores this phenomenon in interprofessional healthcare teams. Specifically, the study focuses on leading through and across boundaries in four interprofessional healthcare teams operating in the area of mental health services. We give special consideration to the specific contexts of these teams, and address the boundaries that are constructed and managed in interactions. Our qualitative study revealed that leadership can be exercised by different members and at different levels within the teams, and that it involves managing the boundaries between (a) roles of different members of the leadership constellation, (b) leadership and clinical roles, (c) formal leaders and other members of the team, (d) different professions, (e) personal life experiences and professional work, and (f) the team and what members consider to be the environment. We identify different types of boundary work tactics that involve opening, closing, and contesting/negotiating boundaries. In addition, we address the potential consequences of each of these tactics. We consider the implications of our findings to leadership research and practice in healthcare contexts and beyond.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".