P.062 A study of stroke-related experiences and priorities of elderly living with dementia, their family caregivers and physicians
Bibliographic record
Abstract
Background: Around 10% of ischemic stroke patients have pre-existing dementia and are excluded from stroke trials and routine care. Little is known about physician practices in the stroke care of people living with dementia (PLWD) leading to limited understanding of their experiences, priorities, and outcomes. This study aims to better understand PLWD through in-depth interviews. Methods: This study employs a qualitative descriptive methodology with two sets of 20 semi-structured interviews with PLWD and their primary caregivers (dyads), and with stroke physicians. Interviews with dyads investigate their experiences, priorities, and attitudes towards stroke care. Participants will be recruited through snowball sampling and interviews will be analyzed through qualitative data analysis software. Results: Initial analyses of the PLWD-caregiver dyad interviews have been completed, revealing themes of independence, uncertainty about the future, and fears of another stroke. Conclusions: As the population ages, stroke teams will likely encounter more PLWD. Engaging PLWD and their caregivers is crucial to better understand their experiences and priorities, which will inform future studies and improve their care. The findings from the dyad and physician interviews will be relevant to a broad audience, including patients, caregivers, physicians, researchers, and policymakers.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".