The Use of Evaluation Panels During the Development of a Digital Intervention for Veterans Based on Cognitive Behavioral Therapy for Insomnia: Qualitative Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Individuals enrolling in the Veterans Health Administration frequently report symptoms consistent with insomnia disorder. Cognitive behavioral therapy for insomnia (CBT-I) is a gold standard treatment for insomnia disorder. While the Veterans Health Administration has successfully implemented a large dissemination effort to train providers in CBT-I, the limited number of trained CBT-I providers continues to restrict the number of individuals who can receive CBT-I. Digital mental health intervention adaptations of CBT-I have been found to have similar efficacy as traditional CBT-I. To help address the unmet need for insomnia disorder treatment, the VA commissioned the creation of a freely available, internet-delivered digital mental health intervention adaptation of CBT-I known as Path to Better Sleep (PTBS). OBJECTIVE: We aimed to describe the use of evaluation panels composed of veterans and spouses of veterans during the development of PTBS. Specifically, we report on the methods used to conduct the panels, the feedback they provided on elements of the course relevant to user engagement, and how their feedback influenced the design and content of PTBS. METHODS: A communications firm was contracted to recruit 3 veteran (n=27) and 2 spouse of veteran (n=18) panels and convene them for three 1-hour meetings. Members of the VA team identified key questions for the panels, and the communications firm prepared facilitator guides to elicit feedback on these key questions. The guides provided a script for facilitators to follow while convening the panels. The panels were telephonically conducted, with visual content displayed via remote presentation software. The communications firm prepared reports summarizing the panelists' feedback during each panel meeting. The qualitative feedback described in these reports served as the raw material for this study. RESULTS: The panel members provided markedly consistent feedback on several elements of PTBS, including recommendations to emphasize the efficacy of CBT-I techniques; clarify and simplify written content as much as possible; and ensure that content is consistent with the lived experiences of veterans. Their feedback was congruent with previous studies on the factors influencing user engagement with digital mental health interventions. Panelist feedback influenced multiple course design decisions, including reducing the effort required to use the course's sleep diary function, making written content more concise, and selecting veteran testimonial videos that emphasized the benefits of treating chronic insomnia symptoms. CONCLUSIONS: The veteran and spouse evaluation panels provided useful feedback during the design of PTBS. This feedback was used to make concrete revisions and design decisions consistent with existing research on improving user engagement with digital mental health interventions. We believe that many of the key feedback messages provided by these evaluation panels could prove useful to other digital mental health intervention designers.
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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 | 0.078 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".