Picturing informed consent: Exploring participatory visual methods to enhance meaningful consent conversations with young people
Bibliographic record
Abstract
Informed consent (IC) is a cornerstone of ethical research, ensuring participants are informed and provide voluntary agreement to participate. Yet IC remains complex, especially in research with young people, and practical examples for engaging youth meaningfully in IC processes are limited. Participatory visual methodologies engage youth as knowledge producers to think creatively, express diverse perspectives, and engage in dialog. This study explores how participatory visual methodologies can involve youth in IC by inviting them to create and discuss visual representations of its key concepts. Grounded in literature on research ethics with young people, we ask: How do young people in Cameroon represent some of the core concepts within IC visually, and what kinds of conversations emerge around these representations? To address these questions, we conducted workshops with 56 young people (10–16 years old) in Cameroon’s Southwest and Northwest Regions. Participants took photos to represent and discuss the benefits and risks of research, voluntary participation, and confidentiality as key elements of IC, and created visual representations of IC forms. Findings show that incorporating visual elements in IC prompted participants to critically engage with IC, facilitating a deeper contextualized and nuanced understanding and meaningful dialog about both the notion of consent and the study they are potentially consenting to. This study presents a case on adolescents and IC in an African context, emphasizing the importance of social and cultural factors in IC and contributing to literature that largely focuses on younger children in Western settings. Integrating participatory visual methods reframed IC as a collaborative, group-centered process rather than a researcher-driven, one-time event. These findings highlight the potential of participatory visual approaches to deepen youth engagement in research ethics, contributing to more equitable and locally relevant research practices.
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 | 0.109 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".