Medical Professional Reports and Child Welfare System Infant Investigations: An Analysis of National Child Abuse and Neglect Data System Data
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
Background: Medical professionals are key components of child maltreatment surveillance. Updated estimates of reporting rates by medical professionals are needed. Methods: We use the National Child Abuse and Neglect Data System (2000-2019) to estimate rates of child welfare investigations of infants stemming from medical professional reporting to child welfare agencies. We adjust for missing data and join records to population data to compute race/ethnicity-specific rates of infant exposure to child welfare investigations at the state-year level, including sub-analyses related to pregnant/parenting people's substance use. Results: =731,705) stemmed from medical professionals' reports. Population-adjusted rates of these investigations stemming doubled between 2010 and 2019 (13.1-27.1 per 1000 infants). Rates of investigations stemming from medical professionals' reports increased faster than did rates for other mandated reporters, such as teachers and police, whose reporting remained relatively stable. In 2019, child welfare investigated ∼1 in 18 Black (5.4%), 1 in 31 Indigenous (3.2%), and 1 in 41 White infants (2.5%) following medical professionals' reports. Relative increases were similar across racial groups, but absolute increases differed, with 1.3% more of White, 1.7% of Indigenous, and 3.1% of Black infants investigated in 2019 than 2010. Investigations related to substance use comprised ∼35% of these investigations; in some states, this was almost 80%. Discussion: Rates of child welfare investigations of infants stemming from medical professional reports have increased dramatically over the past decade with persistent and notable racial inequities in these investigations.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it