Digital Pathways to Wellness Among Youth in Residential Treatment: An Exploratory Qualitative Study
Bibliographic record
Abstract
Digital media use is central for youth as a means to facilitate identity development, social connection, and vocational competence. Emerging literature suggests that the influence of digital media use is more nuanced than the contemporary risk/benefit discourse, particularly for youth who experience social and emotional vulnerability. This youth-centered, developmentally informed study attends to the gap in literature addressing the digital media use experiences among youth in residential treatment (RT). McCracken’s Long Interview Method was utilized to conduct and analyze in-depth interviews with youth ( n = 15) aged 13 to 18 in RT. The analysis involved movement from particular to general coding, applying categorical observations, and thematic comparison of transcripts. Consistent with existing literature on other youth populations, participants reported that digital media use had both beneficial and problematic implications for their well-being. Internet access decreased experiences of isolation and stigma and increased capacity to contend with marginalized identities (e.g., disability, Lesbian, Gay, Bisexual, Transgendered, Queer, Two Spirited Plus [LGBTQ2S+], child welfare guardianship). They reported that following an initial digital disconnect and stabilization, digital media use facilitated pathways toward agency, leadership, and community engagement (e.g., LGBTQ2S+ community, recovery blogs, animal advocacy). The findings suggest that supporting youth in RT to identify their online opportunities and needs can encourage individual growth, wellness, and participation in social change.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".