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Record W4393114884 · doi:10.1111/aphw.12538

Time‐savoring moderates associations of solitude with depressive mood, loneliness, and somatic symptoms in older adults' daily life

2024· article· en· W4393114884 on OpenAlexaff
Miriam Wallimann, Shira Peleg, Theresa Pauly

Bibliographic record

VenueApplied Psychology Health and Well-Being · 2024
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSolitudeLonelinessPsychologyMoodDepressive symptomsContext (archaeology)Clinical psychologySocial isolationDevelopmental psychologyPsychiatryAnxiety

Abstract

fetched live from OpenAlex

Episodes of solitude (being alone and without social interaction) are common in older age and can relate to decreased well-being. Identifying everyday resources that help maintain older adults' well-being in states of solitude is thus important. We investigated associations of daily solitude with subjective and physical well-being under consideration of time-savoring (i.e., attending to positive experiences and upregulating positive emotions). 108 older adults aged 65-92 years (M = 73.11, SD = 5.93; 58% women; 85% born in Switzerland) took part in an app-based daily diary study in 2022. Over 14 consecutive days, participants reported daily solitude, time-savoring, depressive mood, loneliness, and somatic symptoms in an end-of-day diary. Multilevel models revealed that participants reported higher depressive mood and loneliness, but not higher somatic symptoms on days on which they spent more time in solitude than usual. Higher-than-usual daily time-savoring was associated with lower depressive mood, loneliness, and somatic symptoms. Associations of solitude with depressive mood, loneliness, and somatic symptoms were weaker on days on which higher time-savoring than usual was reported. Findings highlight the potential of everyday time-savoring as a resource in older adults in the context of increased solitude.

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)0.000
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 score0.274
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.343
Teacher spread0.330 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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