Child Welfare System Involvement Among Children With Medical Complexity
Bibliographic record
Abstract
Children with medical complexity may be at elevated risk of experiencing child maltreatment and child welfare system involvement, though empirical data are limited. This study examined the extent of child welfare system involvement among children with medical complexity and investigated associated health and social factors. A retrospective chart review of children with medical complexity (N = 208) followed at a pediatric hospital-based complex care program in Canada was conducted. Descriptive statistics and odds ratios using logistic regression were computed. Results showed that nearly one-quarter (23.6%) had documented contact with the child welfare system, most commonly for neglect; of those, more than one-third (38.8%) were placed in care. Caregiver reported history of mental health problems (aOR = 3.19, 95%CI = 1.55-6.56), chronic medical conditions (aOR = 2.86, 95%CI = 1.09-7.47), and interpersonal violence or trauma (aOR = 17.58, 95%CI = 5.43-56.98) were associated with increased likelihood of child welfare system involvement, while caregiver married/common-law relationship status (aOR = 0.35, 95%CI = 0.16-0.74) and higher number of medical technology supports (aOR = 0.75, 95%CI = 0.57-0.99) were associated with decreased likelihood. Implications for intervention and prevention of maltreatment in children with high healthcare needs are discussed.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".