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Record W38922848 · doi:10.1111/gwat.13420

Emotional Geographies of Home: Place Identities Among Senior Women Residing in a Long-Term Care Facility

2012· article· en· W38922848 on OpenAlexaffabout
Małgorzata Milczarek

Bibliographic record

VenueGround Water · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsTerm (time)Long-term careAging in placeGerontologyCare workSociologyGender studiesNursingPsychologyWork (physics)MedicineEngineering

Abstract

fetched live from OpenAlex

This research aimed to document the meanings and identities attached to the concept of ‘home’ among older women residing in long-term care. The study is based upon semi-structured, open-ended interviews with eleven senior women who reside in a long-term care home in The City of London, Ontario. This study contributes towards theoretical and methodological debates by combining critical humanism, feminism and the newly developing body of work called ‘emotional geographies’ in its approach. Along with the interview, the novel method of using the body as an ‘instrument of research’ is utilized (Longhurst, 2008). The methodology allows for ‘emotional spaces’ occupied by the participants to be revealed and documented. Findings problematize and provide nuance to previous studies about ‘home’. In particular, my findings demonstrate that spatialities, temporalities, boundaries, tension, and paradox need to be considered when theorizing, and more importantly, legislating ‘home’ into public policy. The landscape of the long-term care ‘home’ is identified to be located simultaneously and paradoxically ‘elsewhere’ – it is displaces from the ‘concrete’ wall of the long-term care institution – while being closely tied to the concept of a changing and fluid body and boundary zones that the body questions. The findings contribute to social theory about the experience of place, while having practical implications for policymakers, managers of long-term care facilities and senior citizens.

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)0.001
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 score0.404
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.255
Teacher spread0.243 · 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

Citations0
Published2012
Admission routes2
Has abstractyes

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