Consumer-Guided Development of an Engagement-Facilitation Intervention for Increasing Uptake and Adherence for Self-Guided Web-Based Mental Health Programs: Focus Groups and Online Evaluation Survey
Bibliographic record
Abstract
BACKGROUND: Self-guided web-based mental health programs are effective in treating and preventing mental health problems. However, current engagement with these programs in the community is suboptimal, and there is limited evidence indicating how to increase the use of existing evidence-based programs. OBJECTIVE: This study aims to investigate the views of people with lived experience of depression and anxiety on factors influencing their engagement with self-guided web-based mental health (e-mental health) programs and to use these perspectives to develop an engagement-facilitation intervention (EFI) to increase engagement (defined as both uptake and adherence) with these programs. METHODS: A total of 24 community members (female=21; male=3) with lived experience of depression and anxiety or depression or anxiety alone participated in 1 of 4 focus groups discussing the factors influencing their engagement with self-guided e-mental health programs and the appearance, delivery mode, and functionality of content for the proposed EFI. A subsequent evaluation survey of the focus group participants (n=14) was conducted to evaluate the resultant draft EFI. Data were thematically analyzed using both inductive and deductive qualitative methods. RESULTS: Participants suggested that the critical component of an EFI was information that would challenge personal barriers to engagement, including receiving personalized symptom feedback, information regarding the program's content or effectiveness and data security, and normalization of using e-mental health programs (eg, testimonials). Reminders, rewards, feedback about progress, and coaching were all mentioned as facilitating adherence. CONCLUSIONS: EFIs have the potential to improve community uptake of e-mental health programs. They should focus on providing information on the content and effectiveness of e-mental health programs and normalizing their use. Given that the sample comprised predominantly young females, this study may not be generalizable to other population groups. There is a strong value in involving people with a lived experience in the design and development of EFIs to maximize their effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".