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Record W2966975589 · doi:10.1111/nin.12306

The significance of overlooked objects: Materiality and care at home for people with dementia

2019· article· en· W2966975589 on OpenAlexaff
Meiriele Tavares Araújo, Isabela Silva Câncio Velloso, Christine Ceci, Mary Ellen Purkis

Bibliographic record

VenueNursing Inquiry · 2019
Typearticle
Languageen
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsUniversity of VictoriaUniversity of Alberta
Fundersnot available
KeywordsMateriality (auditing)DementiaEthnographyContext (archaeology)Relation (database)PsychologyHealth careReciprocity (cultural anthropology)Object (grammar)NursingSociologyMedicineSocial psychologyPolitical scienceDiseaseGeographyAesthetics

Abstract

fetched live from OpenAlex

An increase in the number of older people with dementia is currently a trend around the world. In low and middle countries, effective public health services are not yet well-developed, and family care-givers may be overwhelmed by the requirements of care. This paper has two purposes: to share findings from an ethnographic study about family dementia care practices in Brazil and to draw attention to the significance of the materiality of care practices in the family context. The study was conducted in Belo Horizonte, Brazil. We describe the care trajectory of one family and, analysing data using the insights of Actor Network Theory, show the significance of objects in a networked relation of care. In particular, we show how incontinence products such as adult diapers assume different positions in the network, acting as either mediator or intermediary of care in relation to other elements of the network composed of family members and their living situation, the person with dementia herself, the doctor, and Brazilian health policies. Although the diaper is often seen as an ordinary object for care providers, its participation in a care network shapes relations, activating conflict, financial constraint, and disciplining the body of the person with dementia.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.609

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 studies0.0010.000
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 head0.036
GPT teacher head0.364
Teacher spread0.328 · 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 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

Citations14
Published2019
Admission routes1
Has abstractyes

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