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Record W2073480336 · doi:10.1177/1471301211398990

Using Photovoice with people with early-stage Alzheimer’s disease: A discussion of methodology

2011· article· en· W2073480336 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDementia · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsLakehead University
Fundersnot available
KeywordsPhotovoiceDementiaDiseaseQualitative researchGerontologyPsychologyMedicineSociologySocial sciencePathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.799
GPT teacher head0.606
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it