The use of information and communication technologies by adolescents living with a mental illness in the past 5 years: Scoping review
Bibliographic record
Abstract
The use of information and communication technologies (ICT) is a huge part of adolescents' lives, especially by those living with a mental illness. However, very few studies explore their experience with the use of ICT and how it affects their health. The purpose of this study was to better understand the use of ICT by adolescents living with a mental illness. A scoping review was undertaken using Arksey and O'Malley's method to explore this understudied topic. The following databases were searched: Medline, CINAHL and Psychology and Behavioural Sciences Collection. Studies published between 2017 and 2022 were included. Data were analysed using a data extraction and an analysis grid developed by the research team. Of 1984 articles, only seven met the inclusion criteria. These articles allowed for a better understanding of the type of mental illness these young ICT users had, the type of ICT they use and their overall experience using ICT. The diagnoses most associated with the use of these ICT were suicidal ideation, depression, anxiety and eating illnesss. Types of ICT used were very diverse and adolescents had both positive and negative experiences using these ICT. Very few interventions using ICT were developed according to the needs of adolescents with mental illness. These adolescents often cope with the help of ICT and can have an overall positive experience. Their experience can also be negative as some of them were exposed to suicide-related and violent content. Future research is needed to better understand the best ICT interventions for these young people.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".