Teaching hospital alternatives for Veterans Health Administration facilities: A Google Maps proximity study
Bibliographic record
Abstract
The United States Veterans Health Administration (VHA) serves more than 9 million enrolled Veterans each year. Although most of the care that the VHA sponsors is delivered within its own facilities, there has been a call for "privatizing" some or all of these services. Under such an arrangement, the Department of Veterans Affairs would pay non-VHA providers to deliver care in facilities open to the general public. Privatization is hotly contested on political grounds and is not resolved. Yet the question whether the VHA should be privatized cannot be resolved without first establishing that this policy change is even feasible. One potential obstacle to privatization would be the lack of nearby alternative facilities to deliver care. To assess for the presence of this impediment, we used Google Maps to measure the travel time between 167 VA hospitals and the teaching hospital nearest to each of them. We determined that the mean travel time between VA hospitals and their nearest teaching hospital was approximately 18 minutes with a median of 10 minutes. All but nine VA facilities were within two hours' travel, and these nine within ten minutes' travel to a tertiary care, nonteaching hospital. These data do not definitively resolve the privatization debate, of course, but do refute the assertion that inpatient VA services cannot be privatized because replacement hospitals are too far away. As shown, that is simply not the case.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".