Care for children with special health care needs in disasters
Bibliographic record
Abstract
There are approximately 80 million children in the United States of America. 12 million are children with special special health care needs(CSHCN) [14]. Problems caused by a disaster, including access to shelter, food, water, and supervision, are only the beginning. These children are also dependent on medications, specialized equipment (which frequently requires a source of electricity to operate), and the knowledge and skill of their family and/or health professional caregivers to keep them alive and healthy. CSHCN have an amplified vulnerability due to the identified problems in organizing and providing care for these populations in recent disasters (e.g. hurricane Katrina) [4]. Therefore, only preparation at all levels of health care and government will mitigate the risk of (or even prevent) instability and mortality in CSHCN as a result of a disaster situation. The pediatric rehabilitation team can have an important role to play by providing guidance and education to families of children with CSHCN on appropriate and meaningful preparedness, participating in community and health care planning, performing primary care, and providing expertise to other care providers during an event. All members of the team, including physical, occupational, and speech therapists, social workers, nursing, etc., should encourage parents of CSHCN to be aware of the issues regarding the care of their child in case of a disaster. 2. Overview of CSHCN and TAC (Technologically Assisted Children)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".