Visual Voices: A Participatory Method for Engaging Adolescents in Research and Knowledge Transfer
Bibliographic record
Abstract
Integrating the expertise and perspectives of adolescents in the process of generating and translating research knowledge into practice is often missed, yet is essential for designing and implementing programs to promote adolescent health. This paper describes the use of the arts-based participatory Visual Voices method in translational research. Visual Voices involves systematic creative writing, drawing, and painting activities to yield culturally relevant information which is generated by and examined with adolescents. Qualitative data products include the created artistic products and transcripts from group discussions of the content developed and presented. Data are analyzed and compared across traditional (e.g., transcripts) and nontraditional (e.g., drawings and paintings) media. Findings are reviewed and interpreted with participants and shared publicly to stimulate community discussions and local policy and practice changes. Visual Voices is a novel method for involving adolescents in translational research though Integrated Knowledge Transfer (IKT), a process for bringing researchers and stakeholders together from the stage of idea generation to implementing evidence-based initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".