Challenges and opportunities in rapid disaster research: lessons from the field in New Mexico and Vanuatu
Bibliographic record
Abstract
Rapid research is essential to assess impacts in communities affected by disasters, particularly those communities made "hard-to-reach" due to their active marginalization across history and in contemporary practices. In this article, we describe two rapid research projects developed to assess needs for and experiences of communities hard-hit by disasters. The first is a project on the COVID-19 pandemic in southern New Mexico (USA) that was developed to provide information to local agencies that are deploying programs to rebuild and revitalize marginalized communities. The second is a project on population displacement due to a volcanic eruption in Vanuatu, a lower-middle income country in the South Pacific, with mental and physical health outcomes data shared with the Vanuatu Ministry of Health. We describe the similar and unique challenges that arose doing rapid research in these two different contexts, the potential broader impacts of the research, and a synthesis of lessons learned. We discuss the challenges of rapidly changing rules and regulations, lack of baseline data, lack of survey instruments validated for specific populations and in local languages, limited availability of community partners, finding funding for rapid deployment of projects, rapidly training and working with research assistants, health and safety concerns of researchers and participants, and communicating with local and international partners. We also specifically discuss how we addressed our own personal challenges while also conducting time-intensive rapid research. In both studies, researchers shared results with governmental and non-governmental partners who may use the data to inform the design of their own relief programs. While different in context, type of disaster, and research strategy, our discussion of these projects provides insights into common lessons learned for working with communities at elevated risk for the worst outcomes during disasters, such as the need for flexibility, compromise, and good working relationships with community partners.
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".