P01-173 - Traumatic Impact on the Young mind in Response to Disasters: an Approach to Psychosocial Intervention
Bibliographic record
Abstract
Background Traumatic events such as tsunamis, wildfires, war, cyclones, etc. have a lasting effect on children's emotional well-being. The psychological impacts are multifactorial & reaction is also varied depending on the individual. Objectives This study will review the psychological impact on children's minds following exposure to traumatic events & will identify the evidence based psychosocial approach of intervention. Methods Critical review of literature on the topic. Results The severity of impact depends on exposure to the traumatic event, but minimum exposure can also be harmful. The level of trauma, proximity, duration of exposure, evidence of psychopathology before trauma exposure & disruption in social support networks consistently emerge as strong predictors of psychopathology following exposure to trauma with response to mood, anxiety or behavioral manifestation. Socioeconomic disadvantages that follow disaster predict long-term problems. A positive correlation between children's & parents symptomatology has been noted. Intervention strategies include screening children at risk,triage & referral,community based intervention & trauma focused treatment programs. CBT emerges as the best validated therapeutic modalities for children experiencing trauma related mood & anxiety symptoms. Conclusion Psychological impact resulting in trauma to children could be enormous. Good psychosocial management following a disaster with effective follow through & broad care planning may lessen the long term impacts.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".