Experiences of Older Veterans Who Participated in a Multicomponent Telehealth Program: Qualitative Program Evaluation
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».