Supporting Physical and Mental Health in Rural Veterans Living With Heart Failure: Protocol for a Nurse-Led Telephone Intervention Study
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) remains a disease of notable disparity for rural veterans, despite recent advancements in clinical treatment. Managing HF in the home is stressful and complex for rural veterans who experience unique barriers to optimal physical and mental health, necessitating adequate support and problem-solving skills. OBJECTIVE: This study aims to (1) adapt, to the rural sociocultural context, a culturally sensitive, tailored, telephone support and problem-solving intervention (CARE-HF [Supporting Physical and Mental Health in Rural Veterans With Heart Failure]) using findings from preliminary qualitative research and (2) evaluate the effects of CARE-HF on problem-solving and physical and mental health outcomes among rural veterans with HF. METHODS: This study involves a repeated-measures, single-group design. The intervention content was adapted and tailored to the rural sociocultural context using preliminary qualitative data and guided by the Theories of Social Problem-Solving and Stress, Appraisal, and Coping. Veterans are recruited from Veterans Administration home-based cardiac rehabilitation clinics, cardiology clinics that serve veterans, veterans-based community resource centers, and social media campaigns. Veterans with HF (N=100) receive the CARE-HF intervention. This nurse-led intervention comprises 8 telephone sessions that use a five-step, problem-solving process to manage common HF problems in the home: (1) identifying the problem and viewing it in a positive manner, (2) goal setting, (3) generating potential strategies for problem management, (4) choosing and implementing strategies to manage the problem, and (5) evaluating strategy effectiveness. Veterans receive initial problem-solving training during the first session, with follow-up sessions focusing on problem-solving skill reinforcement and assisting veterans in applying these principles to manage self-identified, HF-related problems experienced in the home. Data are collected at baseline and 3, 6, 12, and 18 months from baseline on problem-solving and outcomes of interest (ie, HF self-care; HF symptoms; health care utilization; depressive symptoms; anxiety; HF-specific, health-related quality of life; stress; resilience; and coping). Demographic data will be analyzed using descriptive statistics and multilevel growth curve modeling with restricted maximum likelihood estimation to compare a series of models using Akaike information criteria and Bayesian information criteria fit indices while controlling for covariates. RESULTS: Recruitment started in April 2023. As of December 2024, we have enrolled 56 veterans. Recruitment is anticipated to end in June 2025, with data collection continuing until all enrolled veterans have completed the 18-month follow-up period. CONCLUSIONS: Adapting and testing a culturally sensitive, tailored, telephone intervention to aid support and problem-solving in the home has the potential to provide individualized care to rural veterans where they reside, thereby reducing travel burden while also increasing access to evidence-based care programs. If effective, telephone support and problem-solving interventions could be a low-cost, accessible method to improve physical and mental health in rural veterans with HF. TRIAL REGISTRATION: ClinicalTrials.gov NCT05839067; https://clinicaltrials.gov/study/NCT05839067. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63498.
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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 | 0.029 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.009 |
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".