Unraveling the complexities of disaster management: A framework for critical social infrastructure to promote population health and resilience
Bibliographic record
Abstract
Complexity is a useful frame of reference for disaster management and understanding population health. An important means to unraveling the complexities of disaster management is to recognize the interdependencies between health care and broader social systems and how they intersect to promote health and resilience before, during and after a crisis. While recent literature has expanded our understanding of the complexity of disasters at the macro level, few studies have examined empirically how dynamic elements of critical social infrastructure at the micro level influence community capacity. The purpose of this study was to explore empirically the complexity of disasters, to determine levers for action where interventions can be used to facilitate collaborative action and promote health among high risk populations. A second purpose was to build a framework for critical social infrastructure and develop a model to identify potential points of intervention to promote population health and resilience. A community-based participatory research design was used in nine focus group consultations (n = 143) held in five communities in Canada, between October 2010 and March 2011, using the Structured Interview Matrix facilitation technique. The findings underscore the importance of interconnectedness of hard and soft systems at the micro level, with culture providing the backdrop for the social fabric of each community. Open coding drawing upon the tenets of complexity theory was used to develop four core themes that provide structure for the framework that evolved; they relate to dynamic context, situational awareness and connectedness, flexible planning, and collaboration, which are needed to foster adaptive responses to disasters. Seven action recommendations are presented, to promote community resilience and population health.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.011 | 0.060 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| 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".