Adapting Participatory Visual Methods to Online and Hybrid Settings: A Scoping Review and Thematic Analysis
Bibliographic record
Abstract
Since the onset of the COVID-19 pandemic, a growing number of health researchers working with participatory visual methods (PVM) such as Photovoice and Digital Storytelling (DST) have shifted from in-person to online and hybrid settings. The purpose of this scoping review was to explore what the existing methodological literature tells us about these adaptations. Our review was oriented around two research questions: (1) What practices and adaptations have been implemented to create and deliver participatory visual methods projects, namely Photovoice and Digital Storytelling, in online and hybrid settings? (2) What are the ethical and equity considerations for promoting community member engagement in online PVM? We searched six international databases for peer-reviewed methodological articles published in all years, and a total of 32 articles met our inclusion criteria. Findings reveal that many adaptations were focused on methodological experimentation and extra planning and preparation. Ethical and equity considerations for promoting community member engagement focused on opportunities for flexible practice adaptations as well as recognizing potential tensions and tradeoffs. The review findings suggest that while there are reasons to be optimistic about the possibilities for increasing reach, accessibility and inclusivity in online PVM initiatives, ambiguities exist regarding participatory engagement, methodological adherence, and the sustainability and future of these methods in online settings. Future qualitative research should explore the experiences of PVM project teams along with further engagement with the post-pandemic literature as it emerges.
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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 | 0.175 | 0.275 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.032 | 0.036 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".