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Record W2754216572 · doi:10.1017/s0144686x17000952

Dementia in rural settings: examining the experiences of former partners in care

2017· article· en· W2754216572 on OpenAlexaffabout
Rachel Herron, Mark W. Rosenberg

Bibliographic record

VenueAgeing and Society · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsQueen's UniversityBrandon University
Fundersnot available
KeywordsRespite careDementiaFeelingNegotiationNursingPsychologyResistance (ecology)GerontologyMedicineSociologySocial psychology

Abstract

fetched live from OpenAlex

ABSTRACT Informal carers, also referred to as partners in care, provide the bulk of care to people living with dementia across a range of community settings; however, the changing experiences and contexts of providing informal care for people with dementia in rural settings are under-studied. Drawing on 27 semi-structured interviews with former partners in care in Southwestern and Northern Ontario, Canada, we examine experiences of providing and accessing care over the course of the condition and across various settings. Our findings illustrate the challenges associated with navigating the system of care, finding people who understand dementia in the surrounding community, negotiating hours of home support, facing resistance to respite from the person with dementia, and feeling pressured into long-term care. We argue that partners' time, bodies and choices are spatially constrained within rural and small-town settings and the current systems of home, community and long-term care.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0110.008
Scholarly communication0.0050.003
Open science0.0010.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.347
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations23
Published2017
Admission routes2
Has abstractyes

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