Experiences of Older Veterans Who Participated in a Multicomponent Telehealth Program: Qualitative Program Evaluation
Bibliographic record
Abstract
Background Older veterans have greater medical complexity, lower physical function, and less daily physical activity compared to age-matched civilians. Telehealth programs offer promising approaches to address these complex needs and improve access for diverse patient populations. Objective The purpose of this program evaluation was to understand veterans’ experiences of the telehealth program’s quality, feasibility, safety, and effectiveness. Methods Interviews were conducted by a provider and external evaluator who had expertise in qualitative methods; veterans were interviewed following completion of the 12-week program. Questions were designed to explore both positive and negative experiences of the program and its 4 components, which were physical therapy, biobehavioral intervention (coaching), social support, and technology. Interviews were audio recorded and transcribed verbatim. Team-based–directed content analysis, using deductive and inductive thematic analysis, was conducted to identify themes; analysis was supported by structured debriefs following each interview and using Dedoose software. Results Twenty-one veterans enrolled in the program (n=14 completed). All 14 completers and 1 withdrawer completed the interviews (mean 60.4, SD 8.2 minutes); interviewees were mostly male (73.3%), White (60.0%), and non-Hispanic (86.7%). The following 6 domains were identified (subthemes to follow): (1) technology, (2) social network, (3) therapeutic relationship, (4) access, (5) feasibility, and (6) patient characteristics. Technology—although veterans noted varying levels of technology competency and satisfaction, most felt encouraged and held accountable to being active by the technology. Social Network—this domain highlighted themes surrounding veterans’ social support both within and outside of the program, which reportedly enhanced motivation and commitment to regular exercise. Therapeutic Relationship—interviewees shared specific ways that providers significantly contributed to their overall experience and their progress. Access—older veterans described the pros and cons of telehealth and noted the program made it possible to begin physical therapy sooner than they would have in person. Telehealth also made it easier for them to fit physical therapy sessions into their workdays, and for some, it provided a solution to overcome mental and physical health issues precluding in-person care. Feasibility—themes of preparedness, fit with daily routine, manageability, and outcomes of the program emerged. Patient Characteristics—motivation, self-efficacy, attitudes and beliefs, and expectations influenced the perceived benefits, overall experience, and therapeutic relationship experienced by the veterans. Finally, many veterans provided constructive feedback to improve the program (eg, organizing group sessions based on functional ability and further integrating technology and wearable data). Conclusions This program evaluation identified impactful aspects of the telehealth program and mechanisms of how those aspects contributed to participants’ satisfaction and outcomes. Veterans offered suggestions to inform ongoing quality and operational improvements, with implications for staffing, training, and patient engagement. Qualitative feedback from the program evaluation identified additional questions to explore through rigorous qualitative research. Conflicts of Interest None declared.
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".