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Record W4386945478 · doi:10.1080/13607863.2023.2258828

Understanding the social and leisure needs of lonely and socially isolated older adults living in residential care: a qualitative study

2023· article· en· W4386945478 on OpenAlexaff
Geneva Millett, Giselle Franco, Alexandra Fiocco

Bibliographic record

VenueAging & Mental Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLonelinessSocial isolationRecreationPsychologyNomothetic and idiographicQualitative researchThematic analysisCoping (psychology)Isolation (microbiology)Social supportSocial engagementFeelingGerontologySocial psychologyClinical psychologySociologyMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Objectives: Rates of loneliness and social isolation increase following the transition to residential care and are associated with poor health outcomes. One way to mitigate these experiences is through meaningful recreation, however, there is research to suggest that currently available programming does not meet the needs of lonely and socially isolated residents. Therefore, the objectives of the current study were to investigate: (1) the lived experience of loneliness and social isolation in residential care, (2) engagement and use of available resources by these residents, and (3) insights as to how programming can further address their needs.Methods: Qualitative interviews were conducted with ten staff members and 14 residents. Interviews were transcribed and analysed using thematic analysis.Results: Three themes emerged from the data: (1) Feelings of loneliness and social isolation (subthemes: loneliness and social isolation differ, contributors, and coping strategies), (2) Recreation and social participation (subthemes: program engagement and barriers to participation), and (3) Supporting residents’ needs (subthemes: suggestions for meaningful recreation and resources).Conclusion: Loneliness and social isolation are idiographic experiences. Contributors to loneliness and social isolation are also reported as barriers to attending programming. Recommendations are made, many of which rely on greater staffing resources and psychoeducation.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.084
GPT teacher head0.438
Teacher spread0.354 · 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.

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

Citations6
Published2023
Admission routes1
Has abstractyes

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