A typology of care-giving across neurodegenerative diseases presenting with dementia
Bibliographic record
Abstract
ABSTRACT The purpose of this study is to develop and extend our understanding of dementia care-giving by introducing a typology of informal care-giving across four different diseases. Care-giving factors were examined with respect to specific dementia presentation in mild cognitive impairment, Alzheimer's disease, dementia with Lewy bodies and Parkinson's disease-associated dementia. Informal care-giving literature in the four diseases was systematically searched to identify specific disease symptoms and resultant care-giving strains and outcomes. Key concepts were extracted and grouped thematically. The first classification, ‘role-shift’, reflects care-giving where cognitive deterioration results in changing roles, uncertainty and relational deprivation among married partners. The second classification, ‘consumed by care-giving’, refers to those caring for persons with dementia-motor decline that greatly increases worry and isolation. Finally, in the ‘service use’ classification, formal support is needed to help care-givers cope with daily responsibilities and behaviour changes. In each case, the dementia presentation uniquely impacts care-giver strains. A major conclusion is that the same support to all care-givers under the umbrella term ‘dementia’ is unwarranted; the development of targeted support is required.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".