Intimate partner violence-related hospitalizations in Appalachia and the non-Appalachian United States
Bibliographic record
Abstract
The highly rural region of Appalachia faces considerable socioeconomic disadvantage and health disparities that are recognized risk factors for intimate partner violence (IPV). The objective of this study was to estimate the rate of IPV-related hospitalizations in Appalachia and the non-Appalachian United States for 2007-2011 and compare hospitalizations in each region by clinical and sociodemographic factors. Data on IPV-related hospitalizations were extracted from the State Inpatient Databases, which are part of the Healthcare Cost and Utilization Project. Hospitalization day, year, in-hospital mortality, length of stay, average and total hospital charges, sex, age, payer, urban-rural location, income, diagnoses and procedures were compared between Appalachian and non-Appalachian counties. Poisson regression models were constructed to test differences in the rate of IPV-related hospitalizations between both regions. From 2007-2011, there were 7,385 hospitalizations related to IPV, with one-third (2,645) occurring in Appalachia. After adjusting for age and rurality, Appalachian counties had a 22% higher hospitalization rate than non-Appalachian counties (ARR = 1.22, 95% CI: 1.14-1.31). Appalachian residents may be at increased risk for IPV and associated conditions. Exploring disparities in healthcare utilization and costs associated with IPV in Appalachia is critical for the development of programs to effectively target the needs of this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".