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Record W2918467565 · doi:10.1017/s0144686x19000187

Assisted living facilities as sites of encounter: implications for older adults’ experiences of inclusion and exclusion

2019· article· en· W2918467565 on OpenAlexaffabout
Rachel Herron, Laura Funk, Dale Spencer, Meghan Wrathall

Bibliographic record

VenueAgeing and Society · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsCarleton UniversityUniversity of ManitobaUniversity of GuelphBrandon University
Fundersnot available
KeywordsInclusion (mineral)Inclusion–exclusion principleSocial exclusionSociologyNarrativeSpace (punctuation)Inclusion and exclusion criteriaLived experienceGender studiesAssisted livingIndependent livingBridging (networking)Older peopleLiving spaceGerontologyPsychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Abstract Most of the existing literature on inclusion and exclusion among older adults focuses on community-dwelling individuals. In this article, we draw on the results of a comparative case study to explore how older adults in two assisted living settings experience inclusion and exclusion. One site was a low-income facility and the other a higher-end facility in a mid-sized Canadian city. Bridging together geographies of encounter and gerontological approaches on social inclusion, we analyse interviews with tenants and key informants to explore when, where and in what ways these groups experience inclusion and exclusion in these particular settings. Tenants’ narratives reveal how their encounters, and in turn their experiences of exclusion and inclusion are shaped by experiences throughout their lifecourse, the organisation of assisted living spaces, communities beyond the facility, and pervasive discourses of ageism and ‘dementiaism’. We argue that addressing experiences of exclusion for older adults within these settings involves making more time and space for positive encounters and addressing pervasive discourses around ageism and ‘dementiaism’ among tenants and staff.

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 score0.351
Threshold uncertainty score0.760

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 head0.012
GPT teacher head0.283
Teacher spread0.270 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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