A Systematic Review of Health Outcomes Among Disaster and Humanitarian Responders
Bibliographic record
Abstract
Introduction Disaster and humanitarian responders are at-risk of experiencing a wide range of physical and psychological health conditions, from minor injuries to chronic mental health problems and fatalities. This article reviews the current literature on the major health outcomes of responders to various disasters and conflicts in order to better inform individuals of the risks and to inform deploying agencies of the health care needs of responders. METHODS: In March 2014, an EMBASE search was conducted using pre-defined search criteria. Two reviewers screened the resultant 2,849 abstracts and the 66 full-length manuscripts which are included in the review. RESULTS: The majority of research on health outcomes of responders focused on mental health (57 of 66 articles). Posttraumatic stress disorder (PTSD) and depression were the most studied diagnoses with prevalence of PTSD ranging from 0%-34% and depression from 21%-53%. Physical health outcomes were much less well-studied and included a wide range of environmental, infectious, and traumatic conditions such as heat stroke, insect bites, dermatologic, gastrointestinal, and respiratory diseases, as well as burns, fractures, falls, and other traumatic injuries. CONCLUSIONS: The prevalence of mental health disorders in responders may vary more and be higher than previously suggested. Overall health outcomes of responders are likely poorly monitored and under-reported. Improved surveillance systems and risk mitigation strategies should be employed in all disaster and conflict responses to better protect individual responders. Garbern SC , Ebbeling LG , Bartels SA . A systematic review of health outcomes among disaster and humanitarian responders. Prehosp Disaster Med. 2016;31(6):635-642.
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 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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".