The Transformation Experience of the Veterans Health Administration and Its Relevance to Canada
Bibliographic record
Abstract
Over the past few years, there has been a steady stream of visitors to Canada from the US Veterans Health Administration (VA). Led by the former Under Secretary for Health in the Department of Veterans Affairs, Dr. Ken Kizer, they come to tell the remarkable story of how the VA transformed itself from a hospital-based bureaucracy described as "dangerous, dirty and scandal-ridden" to a healthcare system for veterans recognized for its high-quality, patient-centred care. It is a fascinating story of how a publicly funded healthcare service changed its entire approach to patient care with a quality improvement lens at its core. Fifteen years ago, critics of the VA called for its complete privatization as the only solution to fixing its problems. A team of quality champions set out to prove otherwise. Canada has some lessons to learn. The VA is a compelling role model for Canadian reformers, in large measure, due to its public sector character.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.079 | 0.024 |
| Scholarly communication | 0.023 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 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".