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Record W3122018563 · doi:10.2478/mgr-2020-0024

When home becomes a cage: Daily activities, space-time constraints, isolation and the loneliness of older adults in urban environments

2020· article· en· W3122018563 on OpenAlexfundno aff
Bohumil Frantál, Pavel Klapka, Eva Nováková

Bibliographic record

VenueMoravian Geographical Reports · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersUniverzita Palackého v OlomouciYork University
KeywordsLonelinessIsolation (microbiology)Social isolationActive ageingPsychological interventionTRIPS architecturePsychologyMobilitiesDemographic economicsBusinessGerontologyOlder peopleSociologySocial psychologyMedicineEconomicsEngineeringTransport engineering

Abstract

fetched live from OpenAlex

Abstract The geography of ageing is addressed in this article by providing new empirical evidence about the significant role of daily activities on the perceptions of isolation and loneliness. The developed model of socio-spatial isolation is based on data from time-space diaries and questionnaires completed by older adults living in three cities in the Czech Republic. The study suggests that socio-spatial isolation is a multicomponent (consisting of passivity, isolation and loneliness components), place-dependent and gendered issue. The passivity is significantly associated with the income and leisure sport activities. The isolation can be well predicted by the age, gender and education, and the frequency of work and specific leisure activities, which are constrained by health conditions, financial opportunities and spatial mobility. Particularly trips to nature, sport activities, cultural events, get together with friends, and visits to restaurants have a positive effect on reducing isolation. Women, particularly those who raised more children, more likely feel lonely in old age when family contacts are reduced. Visits to restaurants, shopping malls and cultural events have a positive effect on reducing loneliness. A constrained mobility and higher time consumption for necessary activities also proved to be an age-related and gendered problem. In this respect, policy interventions should seek to improve flexible work opportunities, the digital skills of older people, and the accessibility and safety of public transport with regard to perceived constraints, which is gaining in importance in the Covid-19 era.

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 score0.026
Threshold uncertainty score0.999

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 studies0.0000.001
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 head0.008
GPT teacher head0.243
Teacher spread0.235 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

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