Building Resilient Healthcare Teams: Insights from Analogy to the Social Biology of Ants, Honey Bees and Other Social Insects
Bibliographic record
Abstract
The resilience of a healthcare system hinges on the adaptability of its teams. Thus far, healthcare teams have relied on well-defined scopes of practice to fulfill their safety mandate. While this feature has proven effective when dealing with stable situations, when it comes to disruptive events, healthcare teams find themselves navigating a fine balance between safety and resilience. Therefore, a better understanding of how the safety vs resilience trade-off varies under different circumstances is necessary if we are to promote and better train for resilience in modern healthcare teams. In this paper, we aim to bring awareness to the sociobiology analogy that healthcare teams might find useful during moments when safety and adaptability have the potential to conflict. Three principles underpin the sociobiology analogy: communication, decentralization, and plasticity. Of particular interest in this paper is plasticity whereby swapping roles or tasks becomes an adaptive, rather than a maladaptive, response teams could embrace when facing disruptive situations. While plasticity has naturally evolved in social insects, infusing plasticity in healthcare teams requires intentional training. Inspired by the sociobiology analogy, such training must value the ability: a) to read each other's cues and miscues, b) to step aside when others had the necessary skills, even if outside their scope, c) to deviate from protocols, and d) to foster cross-training. If the goal is to increase a team's behavioural flexibility and boost their resilience, this training mindset should become second nature.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".