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Record W2513908424 · doi:10.1093/geronb/gbw098

Activity Engagement and Activity-Related Experiences: The Role of Personality

2016· article· en· W2513908424 on OpenAlexaff
Nicky J. Newton, Jana Pladevall-Guyer, Richard Gonzalez, Jacqui Smith

Bibliographic record

VenueThe Journals of Gerontology Series B · 2016
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsWilfrid Laurier University
FundersNational Institute on Aging
KeywordsConscientiousnessPsychologyExtraversion and introversionPersonalityBig Five personality traitsDevelopmental psychologySocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

Objectives: The associations of personality with activity participation and well-being have been well studied. However, less is known concerning the relationship between personality and specific aspects of activity engagement in older adults. We conducted a fine-grained examination of the effects of extraversion and conscientiousness on reported activity engagement-which we define as participation, time allocated, and affective experience-during 8 everyday activities. Method: Data were obtained using a day reconstruction measure from a subgroup of participants in the 2012 Health and Retirement Study (HRS: N = 5,484; mean age = 67.98 years). Results: We found mixed support for hypotheses suggesting that specific personality traits would be associated with activity participation, time allocated, and activity-affective experience. For example, extraverts were more likely to socialize and experienced higher socializing-related positive affect, but did not spend more time socializing. Discussion: Results are discussed in light of the value of including personality in, and its contribution to, studies of activity engagement in later life. In addition, the need to acknowledge the complexity of the concept of activity engagement in future research is highlighted.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.087
GPT teacher head0.365
Teacher spread0.279 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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