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Record W2148873549 · doi:10.1017/s0144686x1400066x

A typology of care-giving across neurodegenerative diseases presenting with dementia

2014· article· en· W2148873549 on OpenAlexafffund
Kaitlyn P. Roland, Neena L. Chappell

Bibliographic record

VenueAgeing and Society · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Victoria
FundersU.S. National Library of MedicineCanadian Institutes of Health ResearchNational Institutes of HealthMichael Smith Health Research BC
KeywordsDementiaTypologyWorryDiseasePsychologyCognitionDementia with Lewy bodiesMedicineGerontologyClinical psychologyDevelopmental psychologyPsychiatrySociologyPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.296
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2014
Admission routes2
Has abstractyes

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