Bridging complexity theory and resilience to develop surge capacity in health systems
Bibliographic record
Abstract
Purpose Health systems are periodically confronted by crises - think of Severe Acute Respiratory Syndrome, H1N1, and Ebola - during which they are called upon to manage exceptional situations without interrupting essential services to the population. The ability to accomplish this dual mandate is at the heart of resilience strategies, which in healthcare systems involve developing surge capacity to manage a sudden influx of patients. The paper aims to discuss these issues. Design/methodology/approach This paper relates insights from resilience research to the four "S" of surge capacity (staff, stuff, structures and systems) and proposes a framework based on complexity theory to better understand and assess resilience factors that enable the development of surge capacity in complex health systems. Findings Detailed and dynamic complexities manifest in different challenges during a crisis. Resilience factors are classified according to these types of complexity and along their temporal dimensions: proactive factors that improve preparedness to confront both usual and exceptional requirements, and passive factors that enable response to unexpected demands as they arise during a crisis. The framework is completed by further categorizing resilience factors according to their stabilizing or destabilizing impact, drawing on feedback processes described in complexity theory. Favorable order resilience factors create consistency and act as stabilizing forces in systems, while favorable disorder factors such as diversity and complementarity act as destabilizing forces. Originality/value The framework suggests a balanced and innovative process to integrate these factors in a pragmatic approach built around the fours "S" of surge capacity to increase health system resilience.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".