Virtual Photovoice With Older Adults: Methodological Reflections during the COVID-19 Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.096 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it