“I’m New to This”: Navigating Digitally Mediated Photovoice Methods to Enhance Research With Older Adults
Bibliographic record
Abstract
The COVID-19 pandemic forced a shift in long established participatory visual qualitative methods. Some researchers adapted photovoice— which traditionally happens in-person—and used technology to connect with participants referred to as “digitally mediated photovoice”. Collective knowledge about best practices for digitally mediated photovoice to support and enhance research with older adults is in its infancy. Thus, to advance the field, we describe our approach to digitally mediated photovoice with older adults for a study in Vancouver, Canada. We explore participant and researcher reflections with data generated during three sessions over two-and-a-half years during the COVID-19 pandemic. The first two virtual interview sessions used photo elicitation, and the third session was an in-person interactive photography exhibition. We identified three central benefits to using digitally mediated photovoice. This approach 1. built rapport through the shared experience of navigating technology; 2. allowed a rich exchange of information despite physical distancing; and 3. facilitated opportunity for participants to exercise their agency. As we consider constraints for in-person data collection, digitally mediated photovoice may offer an avenue to establish mutually beneficial researcher-participant relationships with older adults. We add to the growing body of literature that addresses how qualitative researchers incorporate technology into the research process to reshape how we understand intimacy and access.
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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 | 0.057 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".