Adolescents’ Perspectives on a Mobile App for Relationships: Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Adolescence can be a fertile time for relationship issues, with interpersonal conflict being a risk factor for poor mental health. Mobile app interventions may have a significant appeal to young people in assisting with relationship distress. However, currently available apps have not been formally evaluated. Youths' perspectives on engaging with mobile technology to assist with relationships are also unknown. OBJECTIVE: This study aimed to examine adolescents' attitudes toward the concept of a mobile phone app for relationship help and support, and whether they would be likely to use such an intervention. METHODS: A cross-sectional Web survey consisting of 42 questions, including 13 free responses, was delivered. The proposed app, including character vignettes, was presented, and participants were asked to indicate whether they had experienced the same relationship issues, whether their peers would relate to the issues, and how helpful they found the proposed advice. Participants were also asked to provide their own suggestions for help, which were analyzed using thematic analyses. RESULTS: A total of 150 adolescents (aged 15 to 18) participated. Overall, 60.7% (91/150) were likely to use an app for relationship problems, and this was not associated with demographics or social support (all P values >.05). Likelihood of app usage was found to be influenced by perceived need for help, personal beliefs about app effectiveness, and whether the app is engaging and easy to use. Overall, adolescents were receptive of the proposed content with an average of 99.3% (149/150), rating the strategies provided as somewhat to very helpful. CONCLUSIONS: Adolescents were likely to use a mobile phone app for relationship support, and use was not influenced by gender, age, social support, or any other background characteristic. Instead, likely use was influenced by need, personal beliefs, usability, and the appropriateness of app content. App developers must address these factors if the app is to have a wide-scale uptake.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".