Preimplementation Evaluation of a Self-Directed Care Program in a Veterans Health Administration Regional Network: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The Veteran-Directed Care (VDC) program serves to assist veterans at risk of long-term institutional care to remain at home by providing funding to hire veteran-selected caregivers. VDC is operated through partnerships between Department of Veterans Affairs (VA) Medical Centers (VAMCs) and third-party Aging and Disability Network Agency providers. OBJECTIVE: We aim to identify facilitators, barriers, and adaptations in VDC implementation across 7 VAMCs in 1 region: Veterans Integrated Service Network (VISN) 8, which covers Florida, South Georgia, Puerto Rico, and the US Virgin Islands. We also attempted to understand leadership and stakeholder perspectives on VDC programs' reach and implementation and identify veterans served by VISN 8's VDC programs and describe their home- and community-based service use. Finally, we want to compare veterans served by VDC programs in VISN 8 to the veterans served in VDC programs across the VA. This information is intended to be used to identify strategies and propose recommendations to guide VDC program expansion in VISN 8. METHODS: The mixed methods study design encompasses electronically delivered surveys, semistructured interviews, and administrative data. It is guided by the Consolidated Framework for Implementation Research (CFIR version 2.0). Participants included the staff of VAMCs and partnering aging and disability network agencies across VISN 8, leadership at these VAMCs and VISN 8, veterans enrolled in VDC, and veterans who declined VDC enrollment and their caregivers. We interviewed selected VAMC site leaders in social work, Geriatrics and Extended Care, and the Caregiver Support Program. Each interviewee will be asked to complete a preinterview survey that includes information about their personal characteristics, experiences with the VDC program, and perceptions of program aspects according to the CFIR (version 2.0) framework. Participants will complete a semistructured interview that covers constructs relevant to the respondent and facilitators, barriers, and adaptations in VDC implementation at their site. RESULTS: We will calculate descriptive statistics including means, SDs, and percentages for survey responses. Facilitators, barriers, number of patients enrolled, and staffing will also be presented. Interviews will be analyzed using rapid qualitative techniques guided by CFIR domains and constructs. Findings from VISN 8 will be collated to identify strategies for VDC expansion. We will use administrative data to describe veterans served by the programs in VISN 8. CONCLUSIONS: The VA has prioritized VDC rollout nationwide and this study will inform these expansion efforts. The findings from this study will provide information about the experiences of the staff, leadership, veterans, and caregivers in the VDC program and identify program facilitators and barriers. These results may be used to improve program delivery, facilitate growth within VISN 8, and inform new program establishment at other sites nationwide as the VDC program expands. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57341.
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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 | 0.078 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".