Applications mobiles pour soutenir la santé mentale des jeunes : opportunités et défis
Bibliographic record
Abstract
Introduction Adolescence is a sensitive period during which many changes occur and may have the potential to affect an individual's cognitive and psychosocial development. It is also during this period that the most abrupt increase in mental health problems is noted. Several initiatives seek to prevent the onset or deterioration of these disorders among young people, and especially those in situations of vulnerability because the emotional or behavioral difficulties they are already displaying or because of the adversities they face. In these efforts, the use of technology is generally perceived as natural, even desirable, among these "digital natives." Objective This critical review examines the strengths and limitations of mobile applications (apps), as documented in the literature, and based in our own experience, to evaluate their potential to support youths' mental health and resilience in times of adversity, and the obstacles most likely to lessen their impacts. Findings Mobile apps, by their format and through the types of usage they provide, allow young people to have access to evidence-based information anchored to their realities. Apps also represent an opportunity to engage certain young people in a process of change or to support them in their request for help. All the more, these new tools are available at all times and are aligned to their needs for autonomy and confidentiality. Many challenges must, however, be overcome to best support young people's mental health through apps, including reliance on scientific validation, the protection of personal data and the capacity to attract and promote engagement in young people. Conclusion The critical analysis of the available literature is intended as a strategic thinking process to further support the development of future apps that will meet the best standards as viewed by a multitude of actors likely to create and use them.
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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".