What criteria are young people using to select mobile mental health applications? A nominal group study
Bibliographic record
Abstract
Objective: The popularity of smartphone technology provides a unique opportunity to make mental health support widely accessible, especially among young people. Despite the promising results of some mobile mental health support applications, the overwhelming number of available applications (apps) on the market makes it difficult to make a choice that will be safe and effective. Currently, widely available tools are either developed by experts, without end user input or are solely based on usability rankings. Thus, it remains unclear what aspects of mental health apps are important for young people. The purpose of this study was to determine what criteria young adults use when they select mental health applications and what is the relative importance of these criteria to inform the development of a user-driven app-rating platform. Methods: We conducted 4 group sessions with 47 youth and young adults aged 15-25 in British Columbia, Canada using a modified nominal group technique. This method allows for establishing the relative importance of criteria in a structured group discussion. We recorded, transcribed and analysed the resulting data using qualitative content analysis and quantitative methods. Results: Criteria that are the most important to young adults when selecting mental health apps include accessibility, security and grounding in scientific evidence. We identified specific aspects of the discussed criteria which were ranked in the order of importance. Conclusion: Consulting end users about their priorities when evaluating mental health apps ensures that their values and priorities are incorporated into future app-rating platforms, alongside expert opinions. The present study also outlines the common contexts in which apps are used as well as their desirable features to inform mental health app development.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".