Using Loose Coupling Theory to Understand Interprofessional Collaborative Practice on a Transplantation Team
Bibliographic record
Abstract
Background: A central paradox dwells at the heart of interprofessional care: the tension between autonomy and interdependence. This report uses an ethnographic study to understand how this tension shapes collaborative practice on a distributed, interprofessional transplant team in a Canadian teaching hospital.Methods & Findings: Over four months, two trained observers conducted an ethnography through 162 observation hours, 30 field interviews and 17 formal interviews with 39 consented participants. Data collection and inductive analysis proceeded iteratively. Loose coupling theory was used as a resource to make sense of key themes. We describe the transplant team as a constellation made up of core, inter-service, and outside hospital dimensions. Next, we trace the nature of coupling activities within and across these dimensions of the team constellation, focusing on recurring communication challenges which can signal the relationship between autonomy and interdependence in collaborative acts.Conclusions: We conclude that coupling is fluid and subject to human agency, and that the tension between autonomy and interdependence can be highly productive. Team members, including patients, may negotiate and construct their relations on an autonomy/interdependence axis for strategic purposes. Far from being trapped in a paradox, team members use autonomy and interdependence as resources to achieve complex goals in collaborative settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".