Child Welfare System Involvement Among Children With Medical Complexity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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 it