Cross-cultural conceptualization of a good end of life with dementia: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Research on the nature of a "good death" has mostly focused on dying with cancer and other life-limiting diseases, but less so on dementia. Conceptualizing common cross-cultural themes regarding a good end of life in dementia will enable developing international care models. METHODS: We combined published qualitative studies about end of life with dementia, focus group and individual interviews with the researchers, and video-conferencing and continuous email discussions. The interviews were audio-recorded and transcribed verbatim. The data were analyzed thematically, and the researchers developed common themes referring to their original studies. RESULTS: Fourteen qualitative researchers representing 14 cross-cultural studies covering qualitative data of 121 people with dementia and 292 family caregivers. The researchers and data were from eight countries UK, The Netherlands, Japan, Portugal, Germany, Canada, Brazil, and Ireland. Three focus groups, five individual interviews, and video-conferencing were conducted and feedback on multiple iterations was gained by 190 emails between May 2019 and April 2020 until consensus was achieved. Nine cross-culturally common themes emerged from the discussions and shared interpretation of the data of persons with dementia and family caregivers. Three represent basic needs: "Pain and Symptoms Controlled," "Being Provided Basic Care," and "A Place like Home." Other themes were "Having Preferences Met," "Receiving Respect as a Person," "Care for Caregivers," "Identity Being Preserved," "Being Connected," and "Satisfaction with Life and Spiritual Well-being." "Care for Caregivers" showed the greatest difference in emphasis across cultures. Good relationships were essential in all themes. CONCLUSIONS: The common cross-cultural themes comprise a framework underpinned by value placed on personhood and dignity, emphasizing that interdependency through relationships is essential to promote a good end of life with dementia. These themes and valuing the importance of relationships as central to connecting the themes could support care planning and further development of a dementia palliative care model. TRIAL REGISTRATION: The Graduate School and Faculty of Medicine Kyoto University (R1924-1).
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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 | 0.032 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.009 |
| 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".