Loss of loved ones or home due to a disaster: Effects over time on distress in immigrant ethnic minorities
Bibliographic record
Abstract
Exposure to mass trauma may bring about increased sensitivity to new or ongoing stressors. It is unclear whether sensitivity to stress associated with ethnic minority/immigrant status may be affected by severe exposure to mass trauma. We examined whether the loss of loved ones or home due to a disaster is associated with more persistent disaster-related distress in ethnic minorities compared with Dutch natives in the Netherlands. In residents affected by a fireworks disaster ( N = 1029), we assessed disaster-related distress after 3 weeks, 18 months, and 4 years. The effects of loss of loved ones or home and ethnic minority/immigrant status on distress were analyzed using latent growth modeling. After controlling for age, gender, education, employment, and post-disaster stressful life events, the loss of loved ones was associated with more persistent disaster-related distress in ethnic minorities compared with natives at 18 months, and the loss of home was associated with more persistent disaster-related distress in ethnic minorities compared with natives between 18 months and 4 years. Our results suggest that the loss of loved ones may increase sensitivity to stress associated with ethnic minority/immigrant status during the early phase of adaptation to a disaster. Loss of home may lead to further resource loss and thereby increase sensitivity to stress associated with ethnic minority/immigrant status in the long term. Efforts to prevent stress-related psychopathology following mass trauma should specifically target ethnic minority groups, notably refugees and asylum seekers, who often experienced multiple losses of loved ones as well as their homes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".