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Record W2538399185

Community-based Lifelong Learning for Promoting Health in Older Adults: A Qualitative Analysis of a Continuing Education Program for Seniors in Toronto

2007· article· en· W2538399185 on OpenAlexaboutno aff
Miya Narushima

Bibliographic record

VenueNew Prairie Press (Kansas State University) · 2007
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsLifelong learningContinuing educationGerontologyAdult educationQualitative researchQualitative analysisCommunity educationMedical educationPsychologyPedagogyMedicineNursingSociologySocial science
DOInot available

Abstract

fetched live from OpenAlex

Abstract: This case study of the Seniors Daytime Program, a continuing education program run by the Toronto District School Board, examines the roles and benefits of community-based lifelong learning for the post-work population and, in particular, seniors at risk from the perspectives of adult education and health promotion. Population Aging and Lifelong Learning Like many countries, Canada is aging. The portion of seniors (aged 65 and over), which comprised about 13 % of the population in 2001, will nearly double by 2031 when the last wave of baby boomers retires (Health Canada, 2002). Older Canadians live relatively healthy lives, with 77 % describing their general health in positive terms. Yet, 71 % of the same group also report chronic pain or some long-term activity restrictions (Statistics Canada, 1999). These figures imply that coping with chronic health problems and functional limitations to maintain the quality of life is a concern for many older Canadians. Given the aging population, the growing social concerns about the rising cost of health care, and the shifting trend to community care with its emphasis on self-reliance, it is important to develop a new perspective which views learning

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.027
GPT teacher head0.376
Teacher spread0.349 · 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 designQualitative
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
Published2007
Admission routes1
Has abstractyes

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Same venueNew Prairie Press (Kansas State University)Same topicChronic Disease Management StrategiesFrench-language works237,207