Health system resilience in countries facing terrorist threats: a scoping review
Bibliographic record
Abstract
The increasing frequency of terrorist events has led to a growing need for healthcare services. Indeed, the unexpected nature of terrorist attacks affects the healthcare system. The number of deaths among victims admitted to healthcare facilities calls into question the ability of health systems to cope with shocks. This article aims to strengthen the understanding of the resilience processes of healthcare systems and identify support strategies for other systems facing significant shocks. A scoping review included empirical research on health system resilience and terrorism threats from peer-reviewed literature was conducted. A comprehensive search strategy was conducted in four electronic databases (Medline/PubMed, CINAHL, Global Health, and PsycInfo) in January 2023. The data was thematically analysed using the Braun and Clarke approach. This enabled us to map, organise and synthesise the results using the WHO building blocks as a framework for analysis. We screened 37 papers and then completed a full-text review of 35 identified as relevant. A total of 33 papers were retained for analysis. The results indicate that when health service utilization and stress are associated with service delivery, human resource capacity erodes, compromising system functioning. In most cases, health services were surprised by events because they lacked a minimum emergency preparedness plan. These systemic shocks reinforced the need to strengthen the health systems? resilience. However, the practical application of interventions to make systems resilient has yet to be clearly defined. Moreover, the absorptive capacities of the healthcare system enable an immediate response to a crisis, drawing on available human and organizational resources. It is also clear that by remaining flexible in organizing services, healthcare systems can strengthen their adaptive capacities to ensure better service delivery. Bringing order to the chaos following a terrorist attack requires discipline and a well-prepared, professional healthcare team.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".