Amplifying the Voices of Youth for Equity in Wellness and Technology Research: Reflections on the Midwest Youth Wellness Initiative on Technology (MYWIT) Youth Advisory Board
Bibliographic record
Abstract
Unlabelled: Incorporating youth perspectives into health research can enhance quality, relevance, and ethics while also providing youth with mentorship, exposure to academic research, and professional development opportunities. This has led to a growing number of youth advisory boards (YABs). However, despite increased attention to YABs, the mentions of YABs remain low in the published research on youth and health. Furthermore, little published work has reflected on the importance of engaging with youth of color in YABs. This is critical both because of the perspectives and insight they bring to the research process and to help close the participation gap in extracurriculars among youth from racial and ethnic minoritized groups. To contribute to the literature on YABs and health equity, we offer an overview and reflection on the development and implementation of the Midwest Youth Wellness Initiative on Technology (MYWIT), a 1-week virtual, financially compensated summer YAB for youth of color aged 13 to 17 years centered on amplifying youth voices on questions related to digital technology and mental health. MYWIT youth advisors successfully codeveloped a novel research question and semistructured interview guide on the topic of navigating social media algorithms. The MYWIT process also highlighted the importance of youth compensation levels, scheduling, recruitment strategies, and overall resource constraints. We hope to encourage researchers to reflect on the value that even short duration YABs can add to the research process and how YABs can be structured to better recruit and support advisors who experience economic, institutional, and structural barriers to participation.
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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 | 0.054 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".