Use of Go-Beyond as a Self-Directed Internet-Based Program Supporting Veterans’ Transition to Civilian Life: Preliminary Usability Study
Notice bibliographique
Résumé
BACKGROUND: The transition from military service to civilian life presents a variety of challenges for veterans, influenced by individual factors such as premilitary life, length of service, and deployment history. Mental health issues, physical injuries, difficulties in relationships, and identity loss compound the reintegration process. To address these challenges, various face-to-face and internet-based programs are available yet underused. This paper presents the preliminary evaluation of "Go-Beyond, Navigating Life Beyond Service," an internet-based psychoeducational program for veterans. OBJECTIVE: The study aims to identify the reach, adoption, and engagement with the program and to generate future recommendations to enhance its overall impact. METHODS: This study exclusively used data that were automatically and routinely collected from the start of the Go-Beyond program's launch on May 24, 2021, until May 7, 2023. When accessing the Go-Beyond website, veterans were asked to complete the Military-Civilian Adjustment and Reintegration Measure (M-CARM) questionnaire, which produces a unique M-CARM profile of results specifying potential areas of need on the 5 domains of the measure. Users were then automatically allocated to Go-Beyond modules that aligned with their M-CARM profile. Additionally, quantitative and qualitative data were collected from a survey on aesthetics, interactivity, user journey, and user experience, which was optional for users to complete at the end of each module. RESULTS: Results show a conversion rate of 28.5% (273/959) from the M-CARM survey to the Go-Beyond program. This rate is notably higher compared with similar internet-based self-help programs, such as VetChange (1033/22,087, 4.7%) and resources for gambling behavior (5652/8083, 14%), but lower than the MoodGYM program (82,159/194,840, 42.2%). However, these comparisons should be interpreted with caution due to the limited availability of published conversion rates and varying definitions of uptake and adoption across studies. Additionally, individuals were 1.64 (95% CI 1.17-2.28) more likely to enroll when they express a need in Purpose and Connection, and they were 1.50 (95% CI 1.06-2.18) times more likely to enroll when they express the need Beliefs About Civilians, compared with those without these needs. The overall completion rate for the program was 31% (85/273) and modules' individual completion rates varied from 8.4% (17/203) to 20% (41/206). Feedback survey revealed high overall user satisfaction with Go-Beyond, emphasizing its engaging content and user-friendly modules. Notably, 94% (88/94) of survey respondents indicated they would recommend the program to other veterans, family, or friends. CONCLUSIONS: The Go-Beyond program may offer promising support for veterans transitioning to civilian life through digital technology. Our study reveals insights on user engagement and adoption, emphasizing the need for ongoing evaluation to further address the diverse needs of military personnel. Future research should explore predictors of engagement, the addition of peer or facilitator support, and the use of outcome measures for effectiveness assessment.
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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 | 0,009 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».