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Record W4362703572 · doi:10.1097/wad.0000000000000540

Behavioral and Brain Correlates of Emotional Distress in Older Adults During COVID-19 Quarantine

2023· article· en· W4362703572 on OpenAlexaff
Ori Benari, Ramit Ravona Springer, Ganit Almog, Iscka Yore, Maya Zadok, Hung‐Mo Lin, Yuxia Ouyang, Mary Sano, Barbara B. Bendlin, Abigail Livny, Anthony Heymann, Michal Schnaider Beeri

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsInstitute of Population and Public Health
FundersNational Institute on Aging
KeywordsAnxietyDistressSadnessCognitionOptimismDepression (economics)PsychologyMedicineClinical psychologyPsychiatryAnger

Abstract

fetched live from OpenAlex

COVID-19 led to unprecedented lockdowns and changes in older adults' lives, especially those with type 2 diabetes who have high risk of complications and mortality. We investigated the associations of cognitive and motor function and gray matter volumes (GMVs) with COVID-19 lockdown-related emotional distress of type 2 diabetes older adults, participating in the Israel Diabetes and Cognitive Decline Study. We administered a questionnaire to obtain information about anxiety, depression, general well-being, and optimism during a mandated lockdown. Lower grip strength before lockdown was associated with increased sadness, anxiety, and less optimism. Slower gait speed was associated with greater sadness. Lower GMV was related to greater anxiety during the lockdown when compared with anxiety levels before the COVID-19 outbreak. Yet, global cognition was not associated with any emotional distress measure. These results support the role of good motor function on emotional well-being during acute stress and GMV as a potential underlying mechanism.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.372
Teacher spread0.340 · 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 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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