Assessing Children's Disaster Reactions and Mental Health Needs: Screening and Clinical Evaluation
Bibliographic record
Abstract
OBJECTIVE: To present a framework for assessing children's disaster reactions and mental health needs. METHODS: We reviewed the relevant literature and clinical experience to identify information on assessment approaches in children and to construct an assessment framework based on disaster exposure. RESULTS: Child disaster mental health assessment includes 2 components-screening and clinical evaluation-but these have not been fully explicated or distinguished in the literature. Screening can be used to assess large numbers of children across exposure groups. Clinical evaluation is appropriate for children who are directly exposed to a disaster, for those whose family members and (or) close associates are directly exposed, and for those who are identified through screening as being at risk for psychiatric disturbance. Clinical evaluation includes a full diagnostic assessment (posttraumatic stress disorder and other disorders) with the goals of identifying psychopathology, determining the need for clinical care, and guiding intervention planning and referral. Children with psychiatric conditions should be referred to treatment, while those with psychological distress but without psychiatric illness may benefit from psychosocial interventions. CONCLUSIONS: Screening is appropriate to identify children at risk for psychiatric disturbance who will need further evaluation to determine diagnosis. Screening should not be used to dictate treatment decisions. Children who screen positive for psychiatric risk should receive a full clinical evaluation. Children determined to be suffering from psychiatric disorders should receive, or be referred for, formal treatment. Children without psychiatric disorders may benefit from psychosocial 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".