Developing a Community of Practice to Provide Care Coordination and Address Health-Related Social Needs for Veterans Receiving Care in Community-Based Settings: Program Development and Survey Study
Bibliographic record
Abstract
BACKGROUND: Approximately half of US veterans receive care outside of US Department of Veterans Affairs (VA) Veterans Health Administration facilities-a proportion expected to rise due to the Promise to Address Comprehensive Toxics Act and expanded use of VA-purchased community care. OBJECTIVE: This paper describes the structure and impact of the Veterans Care Coordination in Community Settings (VetCoor) program. VetCoor was implemented in 2 non-VA community health centers, and we explored setting, staffing, and treatment targets to enhance veteran care and inform broader dissemination. METHODS: VetCoor embedded a coordinator within community-based, non-VA health care settings to improve veteran identification and address unmet medical needs. Coordinators also connected veterans with VA and local resources addressing health-related social needs. VetCoor included training on veteran needs and military culture. It also held a monthly community of practice call where coordinators shared best practices and met with facility representatives to learn about VA services. RESULTS: From May 2021 to September 2023, a total of 220 veterans participated, engaging in 773 sessions. Of these 220 veterans, 73 (33.2%) received VA enrollment assistance; 54 (24.5%) were referred for medical care; and 82 (37.3%) received care coordination, including medication reconciliation assistance. They also received assistance with transportation (46/220, 20.9%) nutrition and food access (42/220, 19.1%), housing and repair (42/220, 19.1%), and utility payment support (31/220, 14.1%). Common barriers to veterans seeking care were perceptions that enrolling in the VA took resources from veterans more in need and confusion regarding discharge papers required for enrollment. CONCLUSIONS: VetCoor supported rural veterans' health care and health-related social needs using dedicated coordinators. This model addresses resource gaps, fosters VA-community collaboration, and aligns with the VA's expanding benefits under the Promise to Address Comprehensive Toxics Act.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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 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".