The Utilization and Adaption of Photovoice With Rural Women Aged 85 and Older
Bibliographic record
Abstract
Photovoice is a qualitative research method that can have very positive outcomes, including making marginalized populations visible. Yet we found that traditional Photovoice methods were not fully effective and needed to be adapted with women aged 85 and older in rural Prince Edward Island, Canada. Concerns that required adaptation were time constraints for the researcher and participants, taking appropriate photographs, balancing power between researcher and participants, and ensuring that the women’s voices were heard and presented clearly for them and their communities. Our purpose in this article is to enrich conversations on applying and adapting Photovoice as a research method with older, rural women. With Photovoice, the women in our study learned to use digital cameras to take photographs and told stories about how and why they made choices for their photographs and how they depicted how they were supported or limited to fulfill their vision of aging in place. We address the key features of the data collection process that contributed to the effective use of Photovoice with this population, including photography training and ethical instructions, guiding them in a process for identifying their most important photographs, working out methods for engaging them in codifying the photographs, and involving them in knowledge mobilization with policy makers directly. In addition, we present key benefits they reported from participation in the Photovoice process and the value of Photovoice for them in influencing policies on aging.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".