“It Helped Me Feel Like a Researcher”: Reflections on a Capacity-Building Program to Support Teens as Co-Researchers on a Participatory Project
Bibliographic record
Abstract
The inclusion of youth voices in research relating to their own daily environments, wellbeing, and development is increasingly recognized as essential to ensuring rigor and success in mobilizing community change. Few studies have qualitatively examined youths' experiences and perceptions in participatory roles. This paper presents insights and lessons learned from a capacity-building program designed and delivered as part of a youth participatory research project, Teens Talk Vaping. Teens Talk Vaping took a by-youth-for-youth research approach to co-produce research about teen vaping to inform evidence-based vaping education materials. The capacity-building program was developed to equip seven teens from the Human Environments Analysis Laboratory Youth Advisory Council (four teen girls, three teen boys; Mage = 17.3) with qualitative research skills to contribute as "teen co-researchers" to all phases of the project, from conceptualization through to dissemination. The teen co-researchers were interviewed at four key phases of the program: qualitative research principles and approaches, data collection, data analysis, and overall reflections. Using thematic analysis, findings revealed the positive implications and practical limitations of the capacity-building program, which may support other academics engaging in participatory methodologies with youth and contribute toward the improvement and enrichment of participatory research opportunities for youth.
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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 | 0.087 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.028 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".