Physical and psychological challenges faced by military, medical and public safety personnel relief workers supporting natural disaster operations: a systematic review
Bibliographic record
Abstract
Abstract Natural disasters, including floods, earthquakes, and hurricanes, result in devastating consequences at the individual and community levels. To date, much of the research reflecting the consequences of natural disasters focuses heavily on victims, with little attention paid to the personnel responding to such disasters. We conducted a systematic review of the challenges faced by military, medical and public safety personnel supporting natural disaster relief operations. Specifically, we report on the current evidence reflecting challenges faced, as well as positive outcomes experienced by military, medical and public safety personnel following deployment to natural disasters. The review included 382 studies. A large proportion of the studies documented experiences of medical workers, followed by volunteers from humanitarian organizations and military personnel. The most frequently reported challenges across the studies were structural (i.e., interactions with the infrastructure or structural institutions), followed by resource limitations, psychological, physical, and social challenges. Over 60% of the articles reviewed documented positive or transformative outcomes following engagement in relief work (e.g., the provision of additional resources, support, and training), as well as self-growth and fulfillment. The current results emphasize the importance of pre-deployment training to better prepare relief workers to manage expected challenges, as well as post-deployment supportive services to mitigate adverse outcomes and support relief workers’ well-being.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".