“Pictures helped me understand it in a way words couldn’t”: Youth reflections participating in a youth-led photovoice study
Bibliographic record
Abstract
INTRODUCTION: People with lived experience of a health issue can be engaged in research to address issues related to social justice, informing change through partnerships and an understanding of community perspectives and needs. Although photovoice has been applied to various disciplines and topics across the health sciences, the concrete design of the photovoice process and participants' experience of engaging in photovoice is not always well documented or understood. OBJECTIVE: This paper describes youth participants' experiences and perspectives with a youth-led photovoice design process on a study regarding COVID-19 vaccine confidence. METHOD: The sample consisted of 27 youth aged 14-24 who reported experiencing mental health and/or substance use challenges [MHSU] during the COVID-19 pandemic and some degree of COVID-19 vaccine confidence. Youth participated in a series of photography workshops, then each attended one of the six focus groups about both the topic and experience of the photovoice project. RESULTS: Four themes were constructed from the data: 1) Participating in a photovoice project was an enjoyable experience that had a positive effect on participants; 2) Shared group experiences contributed to building a safe space for participants; 3) Photography and the photovoice process served as a catalyst for reflection; 4) Photovoice shifted participants' perspectives on both the COVID-19 vaccine and photography. CONCLUSIONS: This project, a youth-engaged and youth-led photovoice study, describes how the photovoice methodology can be applied in a public health context to meaningfully involve young people and impact their lives. By involving youth in the co-construction of the study design and implementation, photovoice research can represent positive and empowering experiences for participants. Bringing together a diverse and multifaceted lived experience engagement research team structure strengthened the design, delivery, analysis, and interpretation of the project.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".