Psychosocial crisis management: Assisting sensory-impaired individuals in case of disasters
Bibliographic record
Abstract
BACKGROUND: Psychosocial crisis management interventions do not sufficiently consider visually impaired and deaf individuals. There are difficulties in accessing the available interventions, and the effectiveness of these interventions seems questionable. The United Nations Convention on the Rights of Persons with Disabilities build on the premises of the inclusive participation in psychosocial intervention after disasters. OBJECTIVE: The objective of this study is to provide recommendations for psychosocial intervention for sensory-impaired individuals after disasters and to raise awareness for professionals working in the field of psychotraumatology. METHODS: A qualitative analysis of semistructured expert interviews and focus groups with professionals in psychotraumatology and sensory-impaired individuals was conducted. This research took place within the European Network for Psychosocial Crisis Management: Assisting Disabled in Case of Disaster (EUNAD), which is funded by the European Commission. RESULTS: There is a need for specific knowledge about how to meet the needs of individuals with sensory loss in order to provide psychosocial crisis management after a disaster. This aspect is not included in the existing psychosocial interventions. CONCLUSION: The EUNAD recommendations are a start to fulfill the obligation to include sensory-impaired individuals in preparations for disaster interventions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".