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The Veterans Health Administration: An American Success Story?

2007· article· en· W1959235893 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMilbank Quarterly · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
FundersYork UniversityUniversity of WashingtonParalyzed Veterans of AmericaHealth Services Research and DevelopmentCommonwealth Fund
KeywordsAdministration (probate law)ReputationHealth careBusinessQuality (philosophy)MedicinePublic administrationEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

The Veterans Health Administration (VHA) provides health care for U.S. military veterans. By the early 1990s, the VHA had a reputation for delivering limited, poor-quality care, which led to health care reforms. By 2000, the VHA had substantially improved in terms of numerous indicators of process quality, and some evidence shows that its overall performance now exceeds that of the rest of U.S. health care. Recently, however, the VHA has started to become a victim of its own success, with increased demands on the system raising concerns from some that access is becoming overly restricted and from others that its annual budget appropriations are becoming excessive. Nonetheless, the apparent turnaround in the VHA's performance offers encouragement that health care that is both financed and provided by the public sector can be an effective organizational form.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.317
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it