Use of mobile apps and technologies in child and adolescent mental health: a systematic review
Bibliographic record
Abstract
QUESTION: This review will aim to critically evaluate the currently available literature concerning the use of online mobile-based applications and interventions in the detection, management and maintenance of children and young people's mental health and well-being. STUDY SELECTION AND ANALYSIS: A systematic literature search of six electronic databases was conducted for relevant publications until May 2019, with keywords pertaining to mental health, well-being and problems, mobile or internet apps or interventions and age of the study population. The resulting titles were screened and the remaining 92 articles were assessed against the inclusion and exclusion criteria with a total of 4 studies included in the final review. FINDINGS: In general, young people seem to engage very well with this type of tools, and they demonstrate some positive effects in emotional self-awareness. There have been some studies about this issue and many of the outcomes were notstatistically significant. However, it is still a sparsely documented area, and more research is needed in order to prove these effects. CONCLUSIONS: Mental health apps directed at young people have the potential to be important assessment, management and treatment tools, therefore creating easier access to health services, helping in the prevention of mental health issues and capacitating to self-help in case of need. However, a limited number of studies are currently available, and further assessments should be made in order to determine the outcomes of this type of interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".