Reminiscing together – Study design of a community based participatory dementia care intervention in two Inuit communities in Greenland
Bibliographic record
Abstract
Introduction: This article outlines the study design for the research and development project “Reminiscing together – care development for people with dementia in Greenland”. Longevity is increasing in the world, including Greenland, and the increase in the number of older people brings with it an increase in the number of people living with dementia. Only few materials and care methods are adjusted to or developed in the Greenlandic Inuit context and knowledge about the disease is scarce. In this study we will investigate images of ageing among care workers and how these images affect the day-to-day care work in two nursing homes in Qeqqata municipality. Methods: We will co-design designated spaces for reminiscence activities for residents in nursing homes and co-create smaller reminiscence actions to be integrated into daily care work. Theoretically we draw on the concepts of Identity and identification from sociology, Reminiscence as practiced cultural history from ethnology and The older person as ‘the other’ inspired by postcolonial feminist theory. Methodologically we will apply ethnographic fieldwork methods including participant observation, in-depth interviews and sharing circles, design thinking and co-design workshops as well as implementation and monitoring tools from promising practices. Dissemination: The results of this multidisciplinary community based participatory project will be disseminated widely in research and practice through articles, guidelines and instructional videos.
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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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".