Supporting Youth Access to Research Dissemination through Digital Media: Analysis of Mental Health Impacts
Bibliographic record
Abstract
Research outputs towards dissemination -such as journal articles and academic conferences -may be difficult to access for marginalized youth, despite the fact that engaging youth in this access can 1) help them equitably benefit from the existing research evidence-base while 2) mobilizing new generations towards research utilization and application.A pilot study was conducted to assess digital media as an alternative tool for disseminating research to marginalized youth.Specifically, this article focuses on the mental health implications of communicating research to marginalized youth via digital media.Grounded in the perspectives of marginalized youth themselves, the three-phase study includes an exploratory literature review, a first round of interviews (n = 5) to refine the interview guide, and a second round of interviews with marginalized youth (n = 8) for a pilot investigation.Mental health impacts are analyzed with six emerging themes, with findings below.First, youth self-censor and can experience constant fear even in expressing support for a piece of digital media.Second, youth report intentionally seeking negative emotional experiences via digital media for personal growth and development.Third, youth can successfully receive transformational knowledge via digital media; yet, the inability to communicate this knowledge to peers and the powerlessness they can experience from being unable to utilize this knowledge can result in greater isolation.Lastly, the intrinsic link between digital media and creation of online communities around a common interest could be further explored towards successful research dissemination and utilization in the future.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".