Bridging complexity theory and resilience to develop surge capacity in health systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it