Associations between adverse childhood experiences and need and unmet need for care coordination
Bibliographic record
Abstract
Introduction: Children exposed to adverse childhood experiences (ACEs) may access multiple systems of care to address medical and social complexities. Care coordination (CC) optimizes health outcomes for children with special health care needs who often use multiple systems of care. Little is known about whether ACEs are associated with need and unmet need for CC. Methods: Use of the 2016-2017 National Survey of Children's Health to identify children who saw ≥1 health care provider in the last 12 months. The study team used weighted logistic regression analyses to examine associations between 9 ACE types, ACE score and need and unmet need for CC. Results: In the sample (N=39,219, representing 38,316,004 US children), material hardship (aOR, 1.50; 95% CI, 1.29-1.75), parental mental illness (aOR, 1.31; 95% CI, 1.07-1.60), and neighborhood violence (aOR, 1.33; 95% CI, 1.01-1.74) were significantly associated with an increased need for CC. Material hardship was also associated with unmet need for CC (aOR, 2.37; 95% CI, 1.80 - 3.11). Children with ACE scores of 1, 2, 3, and ≥4 had higher odds of need and unmet need for CC than children with 0 ACEs. Discussion: Specific ACE types and higher ACE scores were associated with need and unmet need for CC. Evaluating the unique needs of children who endured ACEs should be considered in the design and implementation of CC processes in the pediatric healthcare system.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".