Older Veterans’ Experiences of a Multicomponent Telehealth Program: Qualitative Program Evaluation Study
Bibliographic record
Abstract
BACKGROUND: There are 8.8 million American veterans aged >65 years. Older veterans often have multiple health conditions that increase their risk of social isolation and loneliness, disability, adverse health events (eg, hospitalization and death), mental illness, and heavy health care use. This population also exhibits low levels of physical function and daily physical activity, which are factors that can negatively influence health. Importantly, these are modifiable risk factors that are amenable to physical therapy intervention. We used a working model based on the dynamic biopsychosocial framework and social cognitive theory to conceptualize the multifactorial needs of older veterans with multiple health conditions and develop a novel, 4-component telehealth program to address their complex needs. OBJECTIVE: This study aims to describe veterans' experiences of a multicomponent telehealth program and identify opportunities for quality and process improvement. We conducted qualitative interviews with telehealth program participants to collect their feedback on this novel program; explore their experience of program components; and document perceived outcomes and the impact on their daily life, relationships, and quality of life. METHODS: As part of a multimethod program evaluation, semistructured interviews were conducted with key informants who completed ≥8 weeks of the 12-week multicomponent telehealth program for veterans aged ≥50 years with at least 3 medical comorbidities. Interviews were audio recorded and transcribed. Data were analyzed by a team of 2 coders using a directed content analysis approach and Dedoose software was used to assist with data analysis. RESULTS: Of the 21 individuals enrolled in the program, 15 (71%) met the inclusion criteria for interviews. All 15 individuals completed 1-hour interviews. A total of 6 main conceptual domains were identified: technology, social networks, therapeutic relationship, patient attributes, access, and feasibility. Themes associated with each domain detail participant experiences of the telehealth program. Key informants also provided feedback related to different components of the program, leading to adaptations for the biobehavioral intervention, group sessions (transition from individual to group sessions and group session dynamics), and technology supports. CONCLUSIONS: Findings from this program evaluation identified quality and process improvements, which were made before rigorously testing the intervention in a larger population through a randomized controlled trial. The findings may inform adaptations of similar programs in different contexts. Further research is needed to develop a deeper understanding of how program components influence social health and longer-term behavior change.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".