Learning from the U.S. Department of Veterans Affairs Quality Enhancement Research Initiative: QUERI Series
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
As the recent collection of papers from the Quality Enhancement Research Initiative (QUERI) Series indicates, knowledge is leading to considerable action in the United States (U.S.) Department of Veterans Affairs (VA). The QUERI Series offers clinical researchers, implementation scientists, health systems, and health research funders from around the globe a unique window into the both the practice and science of implementation or knowledge translation (KT) in the VA. By describing successes and challenges as well as setbacks and disappointments, the QUERI Series is all the more useful. From the vantage point of Canadian KT researchers and officials at a national health research funding agency, we offer a number of observations and lessons that can be learned from QUERI. "Knowledge, if it does not determine action, is dead to us." Plotinus (Roman philosopher 205AD-270AD).
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 | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it