“A Crazy Roller Coaster at the End”: A Qualitative Study of Death Preparedness With Caregivers of Persons With Dementia
Bibliographic record
Abstract
INTRODUCTION: Caregivers of persons with dementia experience challenges that can make preparing for end-of-life particularly difficult. Feeling prepared for death is associated with caregiver well-being in bereavement and is promoted by strategies supporting a palliative approach. Further conceptualization of caregiver preparedness for death of persons with dementia is needed to guide the practice of healthcare providers and to inform development of a preparedness questionnaire. OBJECTIVES: We aimed to: 1) explore the end-of-life experiences of caregivers of persons with dementia to understand factors perceived as influencing preparedness; and 2) identify the core concepts (i.e., components), barriers and facilitators of preparedness for death. METHODS: This study used an interpretive descriptive design. Semi-structured interviews were conducted with sixteen bereaved caregivers of persons with dementia, recruited from long-term care homes in Ontario. Data was analyzed through reflexive thematic analysis. FINDINGS: Four themes were interpreted including: 'A crazy rollercoaster at the end' which described the journey of caregivers at end-of-life. The journey provided context for the development of core concepts (i.e., components) of preparedness represented by three themes: 'A sense of control, 'Doing right' and 'Coming to terms'. CONCLUSION: The study findings serve to expand the conceptualization of preparedness and can guide improvements to practice in long-term care. Core concepts, facilitators and influential factors of preparedness will provide the conceptual basis and content to develop the Caring Ahead: Preparing for End-of-Life with Dementia questionnaire.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".