MétaCan
Menu
Back to cohort
Record W1768553353 · doi:10.21225/d5h88j

Creative Retirement: Survey of Older Adults' Educational Interests and Motivations

2004· article· en· W1768553353 on OpenAlexaffvenueabout
Atlanta Sloane-Seale, Bill Kops

Bibliographic record

VenueCanadian Journal of University Continuing Education · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLifelong learningGeneral partnershipSituational ethicsAdult educationPsychologyContinuing educationMedical educationGerontologyAdult LearningAdult learnerPedagogySociologyPublic relationsMedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

The University of Manitoba's Continuing Education Division (CED) and Creative Retirement Manitoba (CRM) formed a partnership to promote applied research on lifelong learning and older adults, to develop new and to complement existing educational activities, and to explore new program models and instructional methods to meet the educational needs of older adult learners. A survey, the first in a larger research project of this partnership, was undertaken to identify the learning interests and motivations of a select group of active older adults who participate in CRM's activities. The results indicate that these learnersprefer to learn only for interest, in non-educational settings or on their own;are interested, motivated, and physically and financially capable;confront situational and institutional barriers to learning; andconsider learning important to their lifestyle.These findings are consistent with the notion that an active lifestyle, including continued learning, may lead to improved quality of life, and better health and wellness for older adults. University continuing education (UCE) has a role to play in developing and supporting learning opportunities and programs for older adult learners, albeit a measured one.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.770

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 communication0.0000.000
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 head0.064
GPT teacher head0.336
Teacher spread0.271 · 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

Citations10
Published2004
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of University Continuing EducationSame topicRetirement, Disability, and EmploymentFrench-language works237,207