Feasibility of "CopeSmart": A Telemental Health App for Adolescents
Bibliographic record
Abstract
BACKGROUND: Early intervention is important in order to improve mental health outcomes for young people. Given the recent rise in mobile phone ownership among adolescents, an innovative means of delivering such intervention is through the use of mobile phone applications (apps). OBJECTIVE: The aim of this study was to evaluate the feasibility of "CopeSmart", a telemental health app developed to foster positive mental health in adolescents through emotional self-monitoring and the promotion of positive coping strategies. METHODS: Forty-three adolescents (88% female) aged 15-17 years downloaded the app and used it over a one-week period. They then completed self-report questionnaires containing both open-ended and closed-ended questions about their experiences of using the app. The app itself captured data related to user engagement. RESULTS: On average participants engaged with the app on 4 of the 7 days within the intervention period. Feedback from users was reasonably positive, with 70% of participants reporting that they would use the app again and 70% reporting that they would recommend it to a friend. Thematic analysis of qualitative data identified themes pertaining to users' experiences of the app, which were both positive (eg, easy to use, attractive layout, emotional self-monitoring, helpful information, notifications, unique) and negative (eg, content issues, did not make user feel better, mood rating issues, password entry, interface issues, engagement issues, technical fixes). CONCLUSIONS: Overall findings suggest that telemental health apps have potential as a feasible medium for promoting positive mental health, with the majority of young people identifying such technologies as at least somewhat useful and displaying a moderate level of engagement with them. Future research should aim to evaluate the efficacy of such technologies as tools for improving mental health outcomes in young people.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".