Surfacing the Voices of People with Dementia: Strategies for Effective Inclusion of Proxy Stakeholders in Qualitative Research
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.
Bibliographic record
Abstract
Best practices for conducting HCI research on dementia care increasingly involve multiple stakeholders and incorporate diverse viewpoints. When done effectively, involving proxy stakeholders such as family members and professionals can help bring forward the voices of people with dementia. However, concrete practical guidance for navigating the challenges of integrating different perspectives is lacking. We critically reflect on our own recent qualitative fieldwork involving participants with dementia, family caregivers, and facilitators at a local social program for people with dementia, re-examining our interview transcripts and observation notes through content analysis. We illustrate practical approaches to prioritizing participants’ voices through concrete excerpts that demonstrate strategies for better managing dynamics, intervening effectively, and engaging all stakeholders in the research process. Our reflections and proposed guidelines can benefit HCI researchers and practitioners working with vulnerable populations. We hope this work will spur further discussion and critique to strengthen and improve research practices in this domain.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it