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Record W2001924626 · doi:10.1080/01924788.2013.816834

The Association Between Lifelong Learning and Psychological Well-Being Among Older Adults: Implications for Interdisciplinary Health Promotion in an Aging Society

2013· article· en· W2001924626 on OpenAlexaffabout
Miya Narushima, Jian Liu, Naomi Diestelkamp

Bibliographic record

VenueActivities Adaptation & Aging · 2013
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsBrock University
Fundersnot available
KeywordsLifelong learningHealth promotionGerontologyPromotion (chess)Association (psychology)Logistic regressionPsychologyDemographicsSuccessful agingSoftware deploymentWell-beingPublic healthCardiovascular healthMedicineSociologyDemographyNursingPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

This study examines the association between lifelong learning and well-being among older adults. We conducted a survey with those older than 60 (n = 699) in a public continuing-education program in Ontario, Canada. Data included learners’ demographics, health, lifestyle, participation patterns, and well-being as measured by the Psychological General Well-Being Index. The results show learners’ positive psychological well-being, physical and social health, and healthy lifestyles despite some risk conditions. A logistic regression analysis suggests a positive association between the duration of learning and well-being. These findings call for tactical deployment of affordable and accessible learning activities as an effective health-promotion strategy.

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)0.004
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 score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.405
Teacher spread0.355 · 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

Citations39
Published2013
Admission routes2
Has abstractyes

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