‘Talking lines’: the stories of diagnosis and support as told by those with lived experience of rare forms of dementia
Bibliographic record
Abstract
BACKGROUND: People living with, or caring for someone with, rare forms of dementia can encounter issues while obtaining a diagnosis and trying to access appropriate support. This can affect their wellbeing, quality of life, social relationships and employment status. This study makes use of an arts-based narrative approach to explore individual accounts of these experiences whilst also exploring how, in telling their stories, those affected by rare forms of dementia might invoke, and situate their stories in relation to, broader cultural narratives around dementia and illness. METHODS: Semi-structured interviews were conducted via video-conferencing software with participants (N = 27), living with, or caring for someone with, a rare forms of dementia. Participants used line drawings to depict their journey from initial symptoms to the present day, followed by prompts to verbally narrate their experiences. All interview transcripts and line drawings were subjected to narrative analysis. Four sets of transcripts and drawings were then subjected to more in-depth analysis. RESULTS: Analysis shed light on the struggles encountered by both care-partners and people with a diagnosis, while navigating a health and social care system that does not always understand their needs. This often led to individuals feeling isolated and unsupported. Accounts also depicted challenges to identity brought on by the process. The moment of diagnosis was also drawn in a complicated light. Individuals found comfort in gaining understanding, but felt fear at recognising upcoming challenges. Participants situated their own accounts against mainstream cultural narratives around what good support for cognitive impairment and dementia might look like, whilst also demonstrating the influential role they took on in pursuing the right care. CONCLUSIONS: The use of line drawing, alongside narrative interviews, allowed participants to tell complicated, sometimes anachronistic, stories about difficult experiences, whilst also reflecting on, and attaching meaning to, them. These stories highlighted pressing gaps in healthcare services and shone a light on the various pieces of collective action individuals were engaged in in order to improve them. Finally, in modelling some elements of the participants' service provision which were working, the narratives pointed to future directions services might move in.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 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; 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".