Defining Rurality in Medicare Administrative 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
Rural beneficiaries make up nearly one quarter of the Medicare population, yet rural providers and patients face specific challenges with health and health care delivery that remain inadequately understood. Health disparities between rural and urban residents are widespread, barriers to health care in rural communities persist, and the rural health care workforce is limited. To better understand and track the relationship between rurality and performance under Medicare's payment programs, researchers must be able to identify rural beneficiaries, providers, and hospitals. Although numerous definitions of rurality are applied across the Medicare program, empirical research is lacking comparing the different definitions of rurality and the impact of their application to quality, outcome, or costs. Definitions that recognize rurality as a graded concept, rather than a dichotomous one, hold promise. Understanding the strengths and limitations of different approaches to identifying rurality will help researchers choose the best method for their particular purpose, and help policymakers interpret studies using these approaches.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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