Media-Induced War Trauma Amid Conflicts in Ukraine
Bibliographic record
Abstract
War could be traumatic. War trauma could often lead to severe and sustained health consequences on people's physical and psychological health. War trauma is often prevalent in people who either participated in the war or lived near conflict zones, such as military professionals, refugees, and health workers. Advances in information and communication technologies, such as the speed, scale, and scope at which people worldwide could be exposed to the near-time happenings of the war, mean that an unprecedented number of people could face media-induced war trauma. Different from war experienced in person, which could be limited in scope and intensity, media-induced war trauma can be substantially more extensive and comprehensive-news reports on the war often cover all aspects and angles possible, possibly paired with disturbing, if not demoralizing, images, repeatedly 24/7. Although media-induced war trauma could have a profound influence on people's mental health, particularly factoring in the compounding challenges caused by the pandemic, there is a dearth of research in the literature. To shed light on this issue, in this article, we aim to examine the implications of media-induced war trauma on people's health and well-being. Furthermore, we discuss the duties and responsibilities of the media industry amid and beyond the current conflicts in Ukraine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".