On collective self‐healing and traces: How can swarm intelligence help us think differently about team adaptation?
Bibliographic record
Abstract
BACKGROUND: Health care teams are increasingly forced to navigate complex challenges to achieve their collective aim of delivering high-quality, safe patient care. The teamwork literature has struggled to develop strategies that promote effective adaptive behaviours among health care teams. In part, this challenge stems from the fact that truly collective adaptive behaviour requires members of the teams to abandon the human urge to act self-sufficiently. Nature contains striking examples of collective behaviour as seen in social insects, fish and bird colonies. This collective behaviour is known as Swarm Intelligence (SI). SI remains poorly described in the health care team literature and its potential benefits hidden. OBJECTIVE: In this cross-cutting edge paper, I explore the principles of SI as they pertain to systemic or collective adaptation in human teams. In particular, I consider the principles of trace-based communication and collective self-healing and what they might offer to team adaptation researchers in medical education. RESULTS: From a SI perspective, a solution to a problem emerges as a result of the collective action of the members of the swarm, not the individual action. This collective action is achieved via four principles: direct and indirect communication, awareness, self-determination and collective self-healing. Among those principles, trace-based communication and collective self-healing have been purposefully used by other industries to foster team adaptation. Trace-based communication relies on leaving 'traces' in the environment to drive the behaviour of others. Collective self-healing is the ability of the swarm to cope with failure and adapt to changes by permitting swarm members to be interchangeable. CONCLUSIONS: While allowing teams to rely on indirect communication and to be interchangeable might create discomfort to our ways of thinking, teams outside health care are demonstrating their value to advance human teamwork. SI offers a helpful analogy and a constructive language for thinking about team adaptation.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".