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Record W2778823909 · doi:10.1186/s13104-017-3099-2

Does being a retired or employed caregiver affect the association between behaviours in Alzheimer’s disease and caregivers’ health-related quality-of-life?

2017· article· en· W2778823909 on OpenAlexafffund
Melissa Majoni, Mark Oremus

Bibliographic record

VenueBMC Research Notes · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of WaterlooWestern University
FundersCanadian Institutes of Health Research
KeywordsAffect (linguistics)DiseaseGerontologyMedicineQuality of life (healthcare)Association (psychology)Alzheimer's diseasePsychiatryPsychologyInternal medicinePsychotherapistNursingCommunication

Abstract

fetched live from OpenAlex

OBJECTIVE: We examined whether caregivers' employment status (i.e., retired or employed) might modify the association between the behaviours of persons with Alzheimer's disease (PwAD) and caregivers' health-related quality-of-life (HRQoL). Data came from a cross-sectional study of the primary informal caregivers of 200 persons with mild or moderate Alzheimer's disease. Caregivers completed the EQ-5D-3L to rate their HRQoL and generate health utility scores, and the Dementia Behaviour Disturbance Scale (DBDS) to assess the degree to which PwAD exhibited each of 28 behaviours. Caregivers' health utility scores were regressed on overall DBDS scores, with caregiver employment status (retired, employed) treated as an effect modifier and confounder in separate regression models. We also controlled for age, sex, income, education, caregivers' relationship to the PwAD, and whether caregivers gave up paid employment/cut down working hours to care for PwAD. RESULTS: Effect modification by caregiver employment status is possible, with the inverse association between DBDS score and health utility score largely existing for retired versus employed caregivers. Research using larger samples and longitudinal data would further inform this area of inquiry.

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)0.007
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.237
GPT teacher head0.476
Teacher spread0.239 · 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.

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

Citations7
Published2017
Admission routes2
Has abstractyes

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