Developing a rural Veterans Affairs health care research program: Diligence and unique resolutions
Bibliographic record
Abstract
LAY SUMMARY Development and growth of a rural Veterans Affairs Medical Center (VAMC) research program is one way to provide best care. This article reports the steps and barriers to building a rural research centre, using examples from western North Carolina. One goal driving the research centre’s creation was to increase under-served communities in the research workforce and among participants enrolled in Veterans Health Administration (VHA) research. The VHA reports health care differences for 4.7 million rural and highly rural Veterans, with rural Veterans using VHA services differently than urban and suburban Veterans. A total of 58% of rural Veterans enroll in the VHA, compared with 37% of urban and suburban Veterans. To achieve optimal Veteran health, all Veteran sub-groups must be adequately represented in clinical research trials, but rural Veterans are currently not equally represented. Research centre development steps included: 1) hiring a program specialist to focus on developmental needs, 2) finding a local program assistant to address the details required to develop a research centre, 3) obtaining a designated regulatory staff member, 4) negotiating staff, space, and focus needs, 5) hiring an experienced researcher to support initial research efforts, and 6) networking with other VAMCs, hospitals, agencies, and universities to create a best-care community.
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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 | 0.096 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".