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Record W4402691776 · doi:10.1017/s0144686x2400031x

Configuring possibilities: day programmes for people living with dementia as technologies in practice - RETRACTED

2024· article· en· W4402691776 on OpenAlexafffund
Holly Symonds‐Brown, Christine Ceci, Wendy Duggleby

Bibliographic record

VenueAgeing and Society · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsDementiaDay to dayGerontologyAssisted livingPsychologyMedicineEngineeringOperations management

Abstract

fetched live from OpenAlex

Abstract There is a need for new imaginaries of care and social health for people living with dementia at home. Day programmes are one ‘care in the community’ solution that requires further theorisation to ensure that its empirical base can usefully guide policy. In this paper we contribute to theorising day programmes through an ethnographic case study of one woman living with dementia at home using a day programme. We collected data through observations, interviews and artefacts. We observed Peg, whose case story is central in this paper, over 9 months for a total of 61 hours at the day programme, as well as during 16 hours of observation at her home and 2 community outings. We use a material semiotic approach to thinking about the day programme as a health ‘technology in practice’ to challenge the taken-for-granted ideas of day programmes as neutral, stable, bounded spaces. Peg’s case story is illustrative of how a day programme and its scripts come into relation with an arrangement of family care and life at home with dementia. At times the configuration of this arrangement works to provide a sort of stabilising distribution of care and space to allow Peg and her family to go on in the day-to-day life with dementia. At other times the arrangement may create limits to the care made possible. We argue that how we conceptualise and study day programmes and their relations to home and the broader care infrastructure affects the possibilities of care they can enact.

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)0.006
metaresearch head score (Gemma)0.008
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 score0.111
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.152
GPT teacher head0.526
Teacher spread0.374 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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