Cyclone Idai–related losses and the coping strategies of adolescent survivors in the Odzi community of Manicaland Province, Zimbabwe
Bibliographic record
Abstract
Climate change has been identified as one of the leading threats to human health in Southern Africa. Climate change–induced natural disasters often leave behind a trail of destruction characterized by multidimensional losses such as loss of infrastructure, physical health, and psychological health. Adolescents are among the most vulnerable groups during and after a natural disaster. This article presents findings of a research whose aim was to establish cyclone Idai–related losses and postdisaster coping strategies among adolescent survivors. Based on qualitative data collected from 15 participants in the Odzi community of Manicaland Province in Zimbabwe, the article reveals that participants experienced diverse disaster-related losses, such as loss of independence and sense of control over their lives and general life satisfaction. The study notes that the impact of disaster-related losses had a toll on adolescents’ psychological, social, and physical well-being. From the study, it is established that in the aftermath of natural disasters, adolescents adopted two-pronged coping strategies, namely, personal coping strategies that include positive thinking and self-talk, and the utilization of social support such as instrumental and counseling support. This study suggests, among other recommendations, school-based training to equip adolescents with personal coping strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".