Bibliographic record
Abstract
BACKGROUND: Appropriate home modifications (HMs) can make the home environment accessible and relatively safe by reducing the risk of falls. Of special concern are individuals living alone, living in rural communities, and/or living in substandard housing. The Home Improvements and Structural Alterations (HISA) is a Veterans Health Administration (VHA) benefit program providing HMs for veterans with disabilities. METHODS: The objective of this study was to detail the profile of rural veteran (RV) HISA users and report on national HISA utilization patterns. We compare use at US Department of Veterans Affairs (VA) medical centers of varying complexity levels, and in VA regions. An examination of the relationship between travel time/distance and HISA utilization is also provided. This retrospective database study uses GeoSpatial analyses and 3 VA sources: The National Prosthetics Patient Database, the VHA Medical Inpatient Dataset, and the VHA Outpatient Dataset. RESULTS: From 2015 through 2018, 10,810 RVs used HISA with a mean age of 70.9 years. A majority of participants were White (79.5%), married (74.3%), and male (96.5%) veterans. They traveled a mean of 79.8 miles for 94.5 minutes to reach a facility where they received a HISA prescription. Nearly 75% of HISA users were able to receive a HISA prescription from their nearest facility, while about one-quarter traveled to a facility farther away, of which 43% travelled between 100 and 200 miles to obtain the HISA benefit. The top categories of diagnoses were musculoskeletal (19.1%), neurologic (12.5%), and cardiovascular (5.4%). There were about 11,166 HM prescriptions afforded to rural HISA users during the period, including bathroom (82.4%), doorway (4.9%), and railing (3.6%) modifications. CONCLUSIONS: This study documents the national demographics and clinical characteristics of rural HISA users, data that may be useful to policy makers, HM service providers and advocate as well as HISA administrators in predicting future use and users.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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 teacher head, 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".