Virtual Photovoice With Older Adults: Methodological Reflections during the COVID-19 Pandemic
Bibliographic record
Abstract
Photovoice is a participatory action research method in which participants take and narrate photographs to share their experiences and perspectives. This method is gaining in popularity among health researchers. Few studies, however, have described virtual photovoice data collection despite the growing interest among qualitative health researchers for online data collection. As such, the aim of this article is to discuss the implementation of a virtual photovoice study and presents some of the challenges of this design and potential solutions. The study examined issues of social isolation and mental health among older adults during the COVID-19 pandemic in the Canadian province of Québec. Twenty-six older adults took photographs depicting their experience of the pandemic that were then shared in virtual discussion groups. In this article, we discuss three key challenges arising from our study and how we navigated them. First, we offer insights into managing some of the technical difficulties related to using online meeting technologies. Second, we describe the adjustments we made during our study to foster and maintain positive group dynamics. Third, we share our insights into the process of building and maintaining trust between both researchers and participants, and amongst participants. Through a discussion of these challenges, we offer suggestions to guide the work of health promotion researchers wishing to conduct virtual photovoice studies, including with older adults.
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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 | 0.115 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.021 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| 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".