Roles of Schools and Educators in Supporting Resilience in Young Children after Disasters
Bibliographic record
Abstract
The increasing frequency and severity of natural and human-induced disasters have had detrimental effects on global populations, resulting in heightened human suffering and disruptions to social structures. This paper explores the multifaceted impact of disasters, encompassing both natural hazards and unnatural disasters, on children and the role of schools and educators in mitigating these effects. While the immediate concern during crises is children’s safety, there are ways to protect and support resilience in young children. Schools hold significant importance as centres for education, socialization, and economic development, and their role in disaster response and recovery is crucial. Schools provide a sense of normalcy and continuity for communities affected by disasters. However, many schools lack the necessary preparedness, and physical structure in some cases, to effectively respond to these events, and educators often find themselves as first responders without adequate training. The paper underscores the need for comprehensive disaster skills training for educators and school staff, ensuring they are equipped to address children’s psychological, emotional, and educational needs before, during and after disasters. Additionally, schools can serve as a ‘place attachment’ for children which aids in disaster preparedness, processing traumatic experiences, and fostering resilience and recovery. This Bridging the Gap paper highlights the urgent need for proactive measures in education including disaster-focused training, robust disaster management plans, and the integration of resilience-building strategies. By prioritizing children’s well-being and leveraging the pivotal role of schools and educators, communities can enhance their capacity to cope with, respond to, and recover from disasters, ultimately promoting greater overall resilience.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".