Exploring the Acceptability, Appropriateness, and Utility of a Digital Single-Session Intervention (Project SOLVE-NZ) for Adolescent Mental Health in New Zealand: Interview Study Among Students and Teachers
Bibliographic record
Abstract
BACKGROUND: Globally, we face a significant treatment gap in mental health care, with extensive wait times, exorbitant prices, and concerns about appropriateness for non-Western clients. Digital single-session interventions (SSIs) may offer a promising alternative. SSIs target particular mechanisms that underlie broad-ranging psychopathology, including deficits in problem-solving skills. OBJECTIVE: Developed in the United States, Project SOLVE is a digital SSI that teaches problem-solving skills to adolescents. This study evaluated the acceptability, appropriateness, and utility of an adapted version, Project SOLVE-NZ, among rangatahi (young people) in Aotearoa New Zealand. Additionally, we evaluated a comparable online activity, Project Success-NZ, as a potential active control condition in a future randomized controlled trial of Project SOLVE-NZ. METHODS: A sample of school students and teachers completed Project SOLVE-NZ and Project Success-NZ. Feedback on the interventions was collected through focus groups and semistructured interviews. Interviews were recorded, transcribed, and analyzed using reflexive thematic analysis. RESULTS: In total, 12 students (aged between 13 and 14 years; female students: n=6, 50%) participated in a focus group, and 8 teachers (teaching experience: mean 8.75, SD 7.96 years; female teachers: n=5, 62.5%) participated in individual interviews. Participants endorsed the sociocultural relevance of Project SOLVE-NZ and Project Success-NZ to rangatahi in Aotearoa New Zealand and viewed all existing adaptations favorably. Participants felt that the interventions would be valuable to a wide range of rangatahi, helping to fill gaps in students' learning and providing benefits to mental health. Participants also believed that the interventions may be particularly relevant for youths experiencing economic hardship. Interestingly, most participants had no preference for either Project SOLVE-NZ or Project Success-NZ, and they believed that both interventions could provide ongoing support to rangatahi throughout the school year. Teachers provided some suggestions on increasing student engagement with the interventions, namely, through increased cultural and gender representation, visual and literacy aids, whakawhanaungatanga (relationship building), and teacher guidance. Overall, interviews revealed that both interventions were perceived as acceptable, appropriate, and useful for rangatahi in New Zealand and highlighted further adaptations that could be made prior to a randomized controlled trial of Project SOLVE-NZ across schools nationwide. CONCLUSIONS: Digital SSIs show promise in addressing the mental health treatment gap for adolescents. Both Project SOLVE-NZ and Project Success-NZ were well-received by students and teachers in Aotearoa New Zealand and may provide benefits to youth mental health. We make the following recommendations for others interested in designing digital SSIs or similar tools for young people: involve rangatahi and relevant stakeholders in the design process, consider how the intervention will be implemented, ensure that the intervention accommodates a range of cognitive abilities, and ensure that the intervention reflects the diversity of rangatahi today.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".