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Record W4283750901 · doi:10.1177/07334648221112425

Social Citizenship Through Out-of-Home Participation Among Older Adults With and Without Dementia

2022· article· en· W4283750901 on OpenAlexafffundabout
Sophie Nadia Gaber, Liv Thalén, Camilla Malinowsky, Isabel Margot‐Cattin, Kishore Seetharaman, Habib Chaudhury, Malcolm P. Cutchin, Sarah Wallcook, Anders Kottorp, Anna Brorsson, Samantha Biglieri, Louise Nygård

Bibliographic record

VenueJournal of Applied Gerontology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsToronto Metropolitan UniversitySimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaHorizon 2020 Framework ProgrammeH2020 Marie Skłodowska-Curie ActionsFamiljen Kamprads StiftelseForskningsrådet om Hälsa, Arbetsliv och VälfärdVetenskapsrådetKarolinska Institutet
KeywordsDementiaCitizenshipResidenceGerontologyDescriptive statisticsSocial engagementPsychologyNursing homesMedicineDemographySociologyNursingDiseasePolitical science

Abstract

fetched live from OpenAlex

There is limited empirical knowledge about how older adults living with dementia enact their social citizenship through out-of-home participation. This study aimed: (a) to investigate out-of-home participation among older adults with and without dementia in four countries and (b) to compare aspects of stability or change in out-of-home participation. Using a cross-sectional design, older adults with mild-to-moderate dementia and without dementia, aged 55 years and over, were interviewed using the Participation in ACTivities and Places OUTside the Home questionnaire in Canada ( n = 58), Sweden ( n = 69), Switzerland ( n = 70), and the United Kingdom ( n = 128). Data were analyzed using descriptive statistics and a two-way analysis of variance. After adjustment for age, diagnosis of dementia and country of residence had significant effects on total out-of-home participation ( p < .01). The results contribute to policies and development of programs to facilitate social citizenship by targeting specific activities and places.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.386

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 integrity0.0000.001
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 head0.057
GPT teacher head0.382
Teacher spread0.325 · 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

Citations7
Published2022
Admission routes3
Has abstractyes

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