Talking Lines: A Research Protocol Integrating Verbal and Visual Narratives to Understand the Experiences of People Affected by Rarer Forms of Dementia
Bibliographic record
Abstract
People affected by rarer forms of dementia often have a long and difficult experience obtaining a diagnosis and appropriate support, impacting family, employment and social relationships, quality of life and wellbeing. For this population progressive cognitive symptoms affect skills other than memory and disproportionately occur under the age of 65 years, often resulting in misdiagnosis and lack of appropriate care pathways. The objective of this study will be to better understand the subjective experience of the time period from first noticing symptoms to obtaining a formal diagnosis, through to accessing support, and onward to the present time. Through the concurrent use of line drawings and video-recorded interviews we will collect the stories of people living with different rarer dementias and/or family members who are care partners in Canada and the United Kingdom. Narrative and visual analysis will be used in parallel to methodologically explore how line drawing and verbal discourse interact and inform each other to construct knowledge, and how the use of drawing lines might enrich research interviews and increase accessibility of research participation. This novel research approach may also have implications for clinical interviewing, support services, and public engagement. To the best of our knowledge, this is the first study to retrospectively explore over time the experiences of people affected by rarer forms of dementia from initial symptoms—to diagnosis—to accessing support—to the present, using visual and verbal methodologies.
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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 | 0.101 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".