Local Macroeconomic Trends and Hospital Admissions for Child Abuse, 2000–2009
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between local macroeconomic indicators and physical abuse admission rates to pediatric hospitals over time. METHODS: Retrospective study of children admitted to 38 hospitals in the Pediatric Hospital Information System database. Hospital data were linked to unemployment, mortgage delinquency, and foreclosure data for the associated metropolitan statistical areas. Primary outcomes were admission rates for (1) physical abuse in children <6 years old, (2) non-birth, non-motor vehicle crash-related traumatic brain injury (TBI) in infants <1 year old (which carry high risk for abuse), and (3) all-cause injuries. Poisson fixed-effects regression estimated trends in admission rates and associations between those rates and trends in unemployment, mortgage delinquency, and foreclosure. RESULTS: Between 2000 and 2009, rates of physical abuse and high-risk TBI admissions increased by 0.79% and 3.1% per year, respectively (P ≤ .02), whereas all-cause injury rates declined by 0.80% per year (P < .001). Abuse and high-risk TBI admission rates were associated with the current mortgage delinquency rate and with the change in delinquency and foreclosure rates from the previous year (P ≤ .03). Neither abuse nor high-risk TBI rates were associated with the current unemployment rate. The all-cause injury rate was negatively associated with unemployment, delinquency, and foreclosure rates (P ≤ .007). CONCLUSIONS: Multicenter hospital data show an increase in pediatric admissions for physical abuse and high-risk TBI during a time of declining all-cause injury rate. Abuse and high-risk TBI admission rates increased in relationship to local mortgage delinquency and foreclosure trends.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".