Earthquakes to Floods: A Scoping Review of Health-related Disaster Research in Low- and Middle-income Countries
Bibliographic record
Abstract
INTRODUCTION: Health-related disaster research is a relatively small; but growing field of inquiry. A better understanding of the scope and scale of health-related disaster research that has occurred in low- and middle-income countries (LMICs) would be useful to funders, researchers, humanitarian aid organizations, and governments as they strive to identify gaps, disparities, trends, and needs of populations affected by disasters. METHODOLOGY: We performed a scoping review using the process outlined by Arksey & O'Malley to assess the characteristics of peer-reviewed publications of empirical health-related disaster research conducted in LMICs and published in the years 2003-2012. RESULTS: Five hundred and eighty-two relevant publications were identified. Earthquakes were by far the most commonly researched events (62% of articles) in the review's timeframe. More articles were published about disasters in China & South Asia/South East Asia than all other regions. Just over half of the articles (51%) were published by research teams in which all the authors' primary listed affiliations were with an institution located in the same country where the research was conducted. Most of the articles were classified as either mental health, neurology and stress physiology (35%) or as traumatology, wounds and surgery (19%). In just over half of the articles (54%), data collection was initiated within 3 months of the disaster, and in 13% research was initiated between 3 and 6 months following the disaster. The articles in our review were published in 282 different journals. DISCUSSION: The high number of publications studying consequences of an earthquake may not be surprising, given that earthquakes are devastating sudden onset events in LMICs. Researchers study topics that require immediate attention following a disaster, such as trauma surgery, as well as health problems that manifest later, such as post-traumatic stress disorder. One neglected area of study during the review's timeframe was the impact of disasters on non-communicable and chronic diseases (excluding mental health), and the management of these conditions in the aftermath of disasters. Strengthening disaster research capacity is critical for fostering robust research in the aftermath of disasters, a particular need in LMICs.
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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 | 0.022 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".