Using Photovoice with people with early-stage Alzheimer’s disease: A discussion of methodology
Bibliographic record
Abstract
Many scholars and activists have challenged researchers to do research ‘with’ not ‘for’ or ‘on’ people with Alzheimer’s disease and related dementias. Photovoice is a relatively new qualitative methodology that involves giving cameras to participants to record and document their experiences in ways that can create change. In this study, the Photovoice method was used with a group of participants in early stages Alzheimer’s disease to explore the use of Photovoice as a methodology with this population. Specifically, I was hoping to understand how Photovoice could be used as a methodology with this group, and to examine the benefits and challenges of using Photovoice with people with Alzheimer’s disease. This paper discusses some of the practical challenges arising out of using this methodology with people with early stage Alzheimer’s disease as well as some of the issues surrounding research ethics, consent, and capacity.
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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 | 0.407 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.009 | 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".