Effective Collaboration Through Activity Theory and Knotworking in Clinical Settings
Bibliographic record
Abstract
Healthcare professionals must be able to work in multidisciplinary teams (MDTs). The purpose of this editorial is to explain how healthcare professionals (can) contribute to the effectiveness of MDT, through the use of activity theory (collective work activity shared by others who are motivated by a purpose mediated by tools in order to achieve a specific goal) and the associated idea of knotworking (method of tying, untying, and retying together seemingly separate threads of activity). The leading thesis here is that MDTs benefit from health professionals with well-established leadership skills, and also strong collaborative skills that enable them to transition fluidly between leadership roles as needed to advance patient care. Within activity theory, knotworking is the process of tying and untying various threads of activity and knowledge from across the MDT in order to accomplish specific objectives over time. Knotworking exemplifies the dynamic nature of MDT collaboration, which requires professionals to be productive in their environment. The viewpoints offered in this editorial contribute to a new perspective on MDTs, one that acknowledges distributed leadership and the importance of co-producing a successful partnership in a clinical setting.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".