Bibliographic record
Abstract
To the Editor: We gladly read Eichbaum’s article,1 which we see as an invitation to integrate conflict into our understandings of interprofessional collaboration or teamwork in health care. We agree with the author that conflict is a fundamental aspect of care delivery, and that its creative potential needs to be better recognized; indeed, after noting the striking absence of power, conflict, and hierarchies in the interprofessional education literature, we made a similar call in 2015.2 We are thus grateful for Eichbaum’s identification of three ways forward for collaboration and teamwork, but would like to stress how the proposed solutions often clash with the context and complexities of care. First, while Eichbaum notes that not all collaborative work is done within teams, he does not tell us how psychological safety, innovation, or the health humanities might help individual workers collaborate safely or innovate when there is no “team” to speak of. In the context of teaching hospitals, where trainees and academic faculty regularly move on and off clinical services, this is a core problem. Finding a way to conceptualize these shape-shifting “teams” as collaborative entities will be essential if we are to develop effective and appropriate education for collaboration. Turning to frameworks such as Hollenbeck and colleagues’3—who define teams by their skill differentiation, authority differentiation, and temporal stability—might be a starting point. Moreover, the use of Steve Jobs to illustrate the importance of collaborative intelligence and the associated trade-offs between agreeableness (of which Jobs had none) and creative nonconformism raises fascinating yet unanswered questions: Where is the line between acceptable and unacceptable behavior? Where does potentially creative conflict stop and destructive rudeness begin? Furthermore, while Eichbaum emphasizes how evaluative systems hinder interpersonal risk taking—including behavior that might seem “unprofessional,” such as speaking up and pushing back—he offers no insight into how we might change these systems to encourage productive conflict and innovation. While solutions to this thorny issue may seem elusive, it is imperative that as an academic community we tackle such concerns head-on, examining both theoretically and empirically how to support innovation and risk taking in these settings. Finally, while we absolutely support the goal of flattening care hierarchies, we doubt that educational interventions alone will suffice here, as we have argued elsewhere.2,4 In sum, we applaud Eichbaum’s piece, and invite our community to tackle the very hard problems of teamwork and collaboration he has raised by confronting, directly, their complexities. Elise Paradis, MA, PhDAssistant professor, Leslie Dan Faculty of Pharmacy, Department of Anesthesia, and Department of Sociology, University of Toronto, and scientist, Wilson Centre, Toronto, Ontario, Canada; [email protected] Cynthia R. Whitehead, MD, PhDAssociate professor, Department of Community and Family Medicine, University of Toronto, director and scientist, Wilson Centre, and vice president for education, Women’s College Hospital, Toronto, Ontario, Canada.
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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.023 | 0.052 |
| Insufficient payload (model declined to judge) | 0.006 | 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".