Academic Health Centers and Humanitarian Crises: One Health System’s Response to Unaccompanied Children at the Border
Bibliographic record
Abstract
University of California Health (UCH) provided a system-wide, rapid response to the humanitarian crisis of unaccompanied children crossing the southern U.S. border in the midst of the COVID-19 pandemic in 2021. In collaboration with multiple federal, state, and local agencies, UCH mobilized a multidisciplinary team to deliver acute general and specialty pediatric care to unaccompanied children at 2 Californian emergency intake sites (EISs). The response, which did not disrupt normal UCH operations, mobilized the capacities of the system and resulted in a safe and developmentally appropriate environment that supported the physical and mental health of migrant children during this traumatic period. The capacities of UCH's 6 academic health centers ensured access to trauma-informed medical care and culturally sensitive psychological and social support. Child life professionals provided access to exercise, play, and entertainment. Overall, 260 physicians, 42 residents and fellows, 4 nurse practitioners participated as treating clinicians and were supported by hundreds of staff across the 2 EISs. Over 5 months and across both EISs, a total of 4,911 children aged 3 to 17 years were cared for. A total of 782 children had COVID-19, most infected before arrival. Most children (3,931) were reunified with family or sponsors. Continuity of care after reunification or placement in a long-term shelter was enhanced by use of an electronic health record. The effort provided an educational experience for residents and fellows with instruction in immigrant health and trauma-informed care. The effort benefitted from UCH's recent experience of providing a system-wide response to the COVID-19 pandemic. Lessons learned are reported to encourage the alignment and integration of academic health centers' capacities with federal, state, and local plans to better prepare for and respond to the accelerating need to care for those in the wake of disasters and humanitarian crises.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| 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".