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Record W3217717707 · doi:10.5770/cgj.24.507

Relationship Between Social Activity and Frailty in Japanese Older Women During Restriction on Outings due to COVID-19

2021· article· en· W3217717707 on OpenAlexvenueno aff
Michiko Akita, Naoto Otaki, Miyuki Yokoro, Megumu Yano, Norikazu Tanino, Keisuke Fukuo

Bibliographic record

VenueCanadian Geriatrics Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGerontologyCoronavirus disease 2019 (COVID-19)Social activityDemographyAssociation (psychology)Older peopleMultivariate analysisCross-sectional studyPsychologySocial psychology

Abstract

fetched live from OpenAlex

Background This study investigated the relationship between social activities and frailty during the restriction on outings due to COVID-19. Design A cross-sectional study. Setting and Subjects This study was conducted in City Nishinomiya of Prefecture Hyogo, in Japan. A mail survey was carried out among women aged 65 years or older in May 2020. A population of 293 women aged 65 years or older living in the community was recruited for the study and 213 of them were analyzed. Measurements The survey included questions on sex, age, height, weight, and social activity. Social activity consisted of participation in social organizations and their frequency, as well as frequency of interaction with family and friends. The survey also asked if regular social activity had been impeded by COVID-19. Results A significant association was found between frailty and hindered interaction with friends (β: 0.176, p = .014). Multivariate linear regression analysis confirmed that this association was also significant in Model 1 (β: 0.158, p = .025), and Model 2 (β: 0.148, p = .034). Conclusions No association between being hindered in social activity and frailty was found in older women living in the community during the restriction on outings due to COVID-19.

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)0.004
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.309
Teacher spread0.262 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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