Using photovoice with older adults: some methodological strengths and issues
Bibliographic record
Abstract
ABSTRACT Photovoice is a qualitative research technique in which participants record and reflect on their community through photography. The technique is gaining popularity as a participatory research methodology. Few studies, however, have described the use of photovoice with older adults. This paper examines the application of photovoice in a qualitative, participatory research study examining age-friendly community characteristics in four communities in Manitoba, Canada. Thirty older adults were provided with cameras and took photographs to illustrate how age-friendly their communities are and participated in group discussions to identify priorities in becoming more age-friendly. The research process and results were analysed in order to assess the application of the methodology with older adults. Photovoice is an effective tool for eliciting older persons’ perceptions of their communities, giving voice to the unique concerns of older adults, and identifying strategies for change. If adapted to accommodate the needs of seniors, this methodology provides an innovative approach to community-based gerontological research. On the other hand, there are a number of challenges to be overcome if photovoice is to be a truly effective research instrument, including recruitment, photography training, retrieving consent forms, and issues of time and distance.
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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 | 0.637 | 0.585 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".