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Record W4387024910 · doi:10.1080/01490400.2023.2261425

The Role of Leisure Engagement in Older Adults Adapting to New Residences and Experiencing Other Transitions during a Pandemic

2023· article· en· W4387024910 on OpenAlexaff
Kristin Prentice, Carri Hand, Laura Misener, Jeffrey Hopkins

Bibliographic record

VenueLeisure Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsPandemicIdentity (music)PsychologyEthnographyAdaptation (eye)GerontologyDevelopmental psychologyCoronavirus disease 2019 (COVID-19)SociologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Older adults experience a transition when moving, and moving during a pandemic creates additional challenges. Leisure activities are linked to providing life satisfaction and maintaining identity among older adults experiencing life transitions such as moving to assisted living, widowhood, and caregiving responsibilities. Despite this link, there is a paucity of literature describing leisure’s role in adapting to new homes. A modified ethnographic approach was used to explore leisure’s role in older adults adapting to moving and other transitions during COVID-19. The analysis revealed that participants used leisure activities to maintain identity and to maintain and establish social circles after moving. We suggest that identity is essential to adaptation and should be added to current conceptualizations. This research has implications for practitioners and policy makers working in the community to address issues older adults may be facing by developing policies and programming that support older adults moving to new neighborhoods.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.308
Teacher spread0.281 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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