Giving Voice to People With Dementia and Their Carers: The Impact of Communication Difficulties on Everyday Conversations
Bibliographic record
Abstract
People living with dementia are an under-served group, whose voices are often excluded from research studies due to their speech, language and communication difficulties. As part of a larger study into language processing in dementia, we invited five people with dementia and their carers to tell us about how dementia impacts on their everyday conversations. We also wanted to gain insights into their views on communication strategies to circumvent these difficulties. Aware of the limitations of a standard focus group methodology for this population, we adapted this approach to provide people with dementia the opportunity to be active research participants. To amplify their voices and to enable carers to be as open as possible we ran the groups separately. Each was facilitated by a speech and language therapist. Both groups used communication accessible materials, to create an inclusive environment that valued contributions from all participants. The topic guide remained the same for all participants, ensuring equity in posing the same core questions. Focus groups were video recorded and transcribed. Reflexive thematic analysis was selected as the most appropriate method to ensure overarching themes identified were based in the data. In our analysis the main theme was sense-making; participants experienced and tried to make sense of dementia through the lens of interaction. Four subthemes were also identified, 1. It’s a journey, 2. You have to make the most of things, 3. Ask the right questions and it just flows-strategies in conversation, and 4. Dealing with people. Multimodal adaptations to a focus group methodology have given voice to people with dementia as well as their carers. They characterise dementia and identify useful strategies based on observations of what changes for them in everyday conversations.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| 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; 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".