“We lost all we had in a second”: coping with grief and loss after a natural disaster
Bibliographic record
Abstract
Natural disasters cause immense suffering among affected communities. Most occur in developing countries, which have fewer resources to respond to the resulting traumas and difficulties. As a consequence, most survivors have to rely on their own coping resources and draw from what support remains within family, social networks and the wider community to manage and deal with their losses and consequent emotional distress. Taking the 2004 Asian tsunami as an example, this article reports findings from a qualitative study designed to investigate how survivors responded in Sri Lanka, and the range of coping strategies adopted and resources mobilized. In-depth interviews were conducted with 38 survivors purposively sampled from the Matara district of southern Sri Lanka. Survivors' accounts emphasized the importance of extended supportive networks, religious faith and practices, and cultural traditions in facilitating recovery and sustaining emotional well-being. Government and external aid responses that promoted these, through contributing to the re-establishment of social, cultural, and economic life, were particularly valued by participants. Recourse to professional mental health care and Western psychological interventions was limited and survivors preferred to seek help from traditional and religious healers. Our findings tentatively suggest that long-term mental health following disaster may, in the first instance, be promoted by supporting the re-establishment of those naturally occurring resources through which communities traditionally respond to suffering.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".