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Record W2517336429 · doi:10.18741/p9qp4m

Promoting Meaning and Life Satisfaction to Older Students through Service Learning in Continuing Education

2016· article· en· W2517336429 on OpenAlexaffvenue
Maureen J. Reed, M. Said Al Hadad

Bibliographic record

VenueJournal of Professional Continuing and Online Education · 2016
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLonelinessService-learningPsychologyLife satisfactionCoping (psychology)Medical educationSelf-esteemService (business)Lifelong learningAdult educationApplied psychologyGerontologySocial psychologyPedagogyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

The objective of this study was to examine whether continuing education that focuses on service learning could provide older students (over the age of 50) with knowledge and skills that increase their life satisfaction, confidence, and community integration. We also examined whether it could provide them with meaningful and purposeful experiences. We surveyed older students prior to a service-learning program on satisfaction with life, self-esteem, extraversion, life purpose, depression, loneliness, and self-control coping. After completing the service-learning program, we again surveyed the older students, using the same measures one year later. We found that the service-learning program benefitted the students in terms of their self-esteem, loneliness, confidence, and skill development. In addition, we learned that those who were less satisfied with their life prior to the service-learning program made the highest gains in life satisfaction following the service-learning program. We conclude that continuing education that focuses on service learning may be one way to provide older students with meaningful and psychologically beneficial social experiences.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.376
Teacher spread0.357 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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