Youth Mental Health Interventions via Mobile Phones: A Scoping Review
Bibliographic record
Abstract
Mobile phone technologies have been hailed as a promising means for delivering mental health interventions to youth and adolescents, the age group with high cell phone penetration and with the onset of 75% of all lifetime mental disorders. Despite the growing evidence in physical health and adult mental health, however, little information is available about how mobile phones are implemented to deliver mental health services to the younger population. The purpose of this scoping study was to map the current state of knowledge regarding mobile mental health (mMental Health) for young people (age 13-24 years), identify gaps, and consider implications for future research. Seventeen articles that met the inclusion criteria provided evidence for mobile phones as a way to engage youth in therapeutic activities. The flexibility, interactivity, and spontaneous nature of mobile communications were also considered advantageous in encouraging persistent and continual access to care outside clinical settings. Four gaps in current knowledge were identified: the scarcity of studies conducted in low and middle income countries, the absence of information about the real-life feasibility of mobile tools, the need to address the issue of technical and health literacy of both young users and health professionals, and the need for critical discussion regarding diverse ethical issues associated with mobile phone use. We suggest that mMental Health researchers and clinicians should carefully consider the ethical issues related to patient-practitioner relationship, best practices, and the logic of self-surveillance.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".