Youth Mental Health in the Digital Age: Canadian Youth Perspectives on the Relationship Between Digital Technology and Their Mental Health
Bibliographic record
Abstract
New generations of youth are coming of age at a time when digital technology is omnipresent, and ever-evolving. It is not yet fully appreciated what effect this level of digital technology use will have on current and future generations. Although not entirely negative, dramatic shifts in human interaction and well-being have already presented themselves. Among these shifts are rising rates of youth struggling with mental health–especially since the COVID-19 pandemic. In this study, we explore youth perspectives on the relationship between their digital technology and their mental health, through the use of semi-structured interviews and thematic analysis. Our research question was: How do youth understand the relationship between digital technology use and their mental health? We interviewed eight adolescents and asked them to share their experiences of the relationship between their devices and their well-being. Thematic findings highlight a conflictual relationship between digital technology use and youth mental health, affecting their relationships with others, themselves, and the world around them. Because digital technology consumption on this scale is so new, our sample represents one of the first available cohorts of youth to actively participate in the exploration of this topic. Implications include the need for further qualitative research to prioritize youth voices in ways that will benefit broader societal understandings of technology and mental health.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".