Use of Go-Beyond as a Self-Directed Internet-Based Program Supporting Veterans’ Transition to Civilian Life: Preliminary Usability Study
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".