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Record W2343844977

Long-term care:inside and out

2015· article· en· W2343844977 on OpenAlexaff
Lupin Battersby, Sarah L. Canham, Ryan Woolrych, Mei Lan Fang, Judith Sixsmith, Andrew Sixsmith

Bibliographic record

VenueDiscovery Research Portal (University of Dundee) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsNeighbourhood (mathematics)ResidenceDowntownLong-term careAutonomyContext (archaeology)DignityGerontologyPsychologyMedicineNursingGeographySociologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Institutional long-term care (LTC) settings have been acknowledged as environments that detract from older adults’ health and well-being; diminish resident dignity, personal autonomy, and choice; and result in loss of personhood. However, the neighborhood surrounding a LTC residence can also influence the health and well-being of older adults and their caregivers and therefore requires consideration. To explore perceptions of the neighbourhood context of two institutional settings located in a downtown urban setting, we collected and thematically analysed in-depth interviews from 21 older residents, 22 of their family members, and 32 care staff. Participants reported both positive and negative features of the urban neighbourhood setting. On the one hand, the neighbourhood was described as noisy, dirty, and rough, with the presence of drug use; on the other, the LTC residences benefited from close proximity to cafes and restaurants. Participants suggested that the downsides to being located in the downtown core have negative implications for residents’ independence and safety. For instance, the ability of staff and family members to walk with the residents around the neighbourhood is limited by safety concerns. Instead, resident activities are largely confined to the indoor spaces of the LTC residence. The lack of mobility of LTC residents to areas outside has implications for both city planning and public health, which will be discussed in this presentation. In order to optimize the experience of living in a LTC setting, and thus improving the health and well-being of residents, both indoor and outdoor spaces need to be considered.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.965

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 studies0.0010.001
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 head0.089
GPT teacher head0.358
Teacher spread0.269 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

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