Paradoxical Truths and Persistent Myths: Reframing the Team Competence Conversation
Bibliographic record
Abstract
Medicine has conventionally had an individualist orientation to competence. Individual competence is conceptualized as a stable possession that, once acquired, holds across contexts. Individual competence is necessary; however, it is insufficient for quality health care. We also need to attend to collective competence in order to grapple with paradoxical truths about teamwork, such as: competent individuals can form incompetent teams. Collective competence is conceptualized as a distributed capacity of a system, an evolving, relational phenomenon that emerges from the resources and constraints of particular contexts. This article outlines a set of paradoxical truths about teamwork in health care and uses the concept of collective competence to explain how they can hold true. It then considers a set of persistent myths about teamwork which have their roots in an individualist orientation, exploring how they hold us back from meaningful change in how we educate for, and practice as, health care teams. Finally, the article briefly considers the implications of these truths and myths for educational issues such as interprofessional education and competency-based health professional education.
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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 | 0.050 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.023 | 0.148 |
| Scholarly communication | 0.025 | 0.055 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.013 | 0.031 |
| Insufficient payload (model declined to judge) | 0.003 | 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".