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Record W4210283875 · doi:10.1177/14713012211053970

Brothers and sisters sharing in the care of a parent with dementia

2022· article· en· W4210283875 on OpenAlexaff
Kristina M. Kokorelias, Nira Rittenberg, Natasha T Chin Wan, Jennifer Machon, Yasmin Arfeen, Jill I. Cameron

Bibliographic record

VenueDementia · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsDementiaPsychologyGerontologyMedicineDevelopmental psychologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Brothers' and sisters' experiences providing care to a parent with dementia differ, but little is known about how mixed-gender siblings share their caregiving responsibilities or how sharing affects their relationship. This study aimed to explore mixed-gender siblings processes for distributing caregiving tasks when caring for a parent with dementia and the impact of sharing care on their relationship. This descriptive qualitative study recruited fourteen English-speaking mixed-gender sibling pairs caring for a parent with dementia. Online open-ended surveys and individual semi-structured interviews were completed. Interviews and surveys explored division of caregiving responsibilities, conflict resolution, and the effects of sharing care on sibling relationships. Thematic analysis was used to analyze the qualitative data. Five themes were identified: goal of shared caregiving is to meet parents' needs, sisters often take the lead, practical issues affect sharing of caregiving activities, personal resources or skills affect division of responsibilities, and shared caregiving influences relationship quality. Understanding how siblings share caregiving responsibilities can inform the practices of healthcare professionals who care for people with dementia and their family caregivers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.546

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 head0.014
GPT teacher head0.260
Teacher spread0.247 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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