MétaCan
Menu
← Back to cohort
Record W2985246623 · doi:10.1093/geroni/igz038.3416

SELF-ACCEPTANCE BUFFERS NEGATIVE SOLITUDE-PHYSICAL ACTIVITY LINKS IN OLD AGE

2019· article· en· W2985246623 on OpenAlexaff
Jody Mielcarski, Peter Graf, Maureen C. Ashe, Christiane A. Hoppmann

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSolitudeLonelinessPsychologyContext (archaeology)Developmental psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Loneliness is positively associated with a number of negative psychological and health outcomes. Solitude, a related yet distinct phenomenon, can have positive or negative ramifications depending on the context. As older adults spend significant time in solitude, there is particular need to investigate the effects of solitude on the health of this specific segment of the population. This study investigated everyday life associations between solitude and obstacles to physical activity as well as resources for overcoming these obstacles in order to determine whether and for whom solitude is negatively or positively associated with physical activity. Multilevel modelling was used to analyze data from 138 community-dwelling adults aged 65 years and older. Participants completed three daily questionnaires over a period of ten days concerning social context, activities and obstacles, as well as managing obstacles. Preliminary analyses using a subset of participants with complete data (N = 93) indicate that participants reported more physical activity obstacles when they were in solitude. This only applied to participants low in self-acceptance. Furthermore, self-acceptance was also positively associated with the extent to which individuals who had experienced an obstacle (N = 71) managed to overcome it. Further analyses will examine accelerometry-based movement information as well as the role of additional resources (e.g. living with others) and vulnerability factors (loneliness, anxiety).

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)0.001
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.366
Teacher spread0.336 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInnovation in Aging→Same topicHealth disparities and outcomes→French-language works237,207→