Participatory Action Research and Knowledge Dissemination in Virtual Photovoice: Methodological Insights
Bibliographic record
Abstract
Despite the methodological spread of virtual photovoice, alignments to and potential advances for the participatory action research (PAR) and knowledge dissemination (KD) components of in-person photovoice are poorly understood. Detailing the PAR and KD processes, practices, and products drawn from a virtual photovoice study examining men’s experiences of and perspectives about equitable intimate partner relationships, the current article offers three thematic findings. The first theme Processes and pragmatics for selecting representative photographs describes adapting established analytics of preview, review, and cross-photo comparisons to categorize and select images from a large collection of participant-produced photographs ( n = 714). Specifically, detailed are the reconciling of researchers deciding which images and accompanying narratives to include guided by PAR principles. Theme 2, Democratizing and disrupting in-person PAR with virtual focus group polls (VFGPs) , chronicles participant voting through Zoom to collectively decide and subsequently discuss their favorite photographs. While anonymity for the poll was democratizing in terms of participant equality for voting on the photographs, connecting men virtually from diverse locales could differentiate cultural norms. The third theme KD pledges and pitfalls with online photovoice exhibitions details the potential benefits and challenges for reaching diverse end-users. Evident was the importance of marketing and media for driving traffic to the online exhibition, and the centrality of interactivity for fostering engagement to build and adjust photovoice e-health interventions. With virtual photovoice continuing to grow in popularity post COVID-19, this article offers important methodological lessons for adapting and advancing components of in-person PAR and KD.
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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 | 0.235 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".