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Record W2012788641 · doi:10.1177/0898264314535632

Aging and Place in Long-Term Care Settings

2014· article· en· W2012788641 on OpenAlexaff
Robin P. Bonifas, Kelsey Simons, Barbara Biel, Christie Kramer

Bibliographic record

VenueJournal of Aging and Health · 2014
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsSocializationLong-term careThematic analysisContext (archaeology)PsychologyQualitative researchSocial environmentGerontologyQuality of life (healthcare)Aging in placeSocial psychologyDevelopmental psychologySociologyNursingMedicineGeography

Abstract

fetched live from OpenAlex

OBJECTIVE: This article presents results of a qualitative research study that examined how living in a long-term care (LTC) home influences the quality of residents' relationships with peers, family members, and outside friends. METHOD: Semistructured interviews using a phenomenological approach were conducted with 23 residents of a LTC home. Thematic analysis was employed to illuminate residents' perspectives on the nature of social relationships in this setting. RESULTS: Four key themes were identified that highlight the role of place in social relationships. Residing in a LTC home influences the context of social interactions, impacts their quality and process, clusters individuals with health and functional declines that hinder socialization, and poses structural and cultural barriers that impede social interactions. Health and functional limitations posed the greatest challenge to socialization relative to characteristics of the facility itself. DISCUSSION: Residents' insights emphasize how personal characteristics influence community culture and the experience of place.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.006
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.404
Teacher spread0.376 · 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 source (direct Gemma or distilled Codex), 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

Citations39
Published2014
Admission routes1
Has abstractyes

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