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Record W4408257076 · doi:10.1080/23311886.2025.2473640

A discourse on healthy ageing in Newfoundland and Labrador, Canada

2025· article· en· W4408257076 on OpenAlexaffabout
Paul Alhassan Issahaku

Bibliographic record

VenueCogent Social Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAgeingHealthy ageingPolitical scienceGeographyGender studiesSociologyHistoryMedicine

Abstract

fetched live from OpenAlex

Newfoundland and Labrador are experiencing an increase in the population of older adults, and this invites stakeholders to reflect on how to promote health and vitality. This discourse analysis study contributes to existing literature and provides information that is relevant for deliberations on healthy ageing. A purposive sample of 15 participants aged 65 years and above, recruited across the island of Newfoundland, provided interview data, which were analyzed following discourse analysis guidelines. A healthy ageing discourse is presented which extends the literature and current perspectives in Canada. The findings show that healthy ageing is not an either/or outcome, and is less a function of personal effort. Instead, healthy ageing involves ageing with the expected strengths and limitations, but with the support, services, and social connections of one’s natural community, which enable one to make the most of older age. The findings further suggest that an older-adult-friendly healthcare system is essential for healthy ageing. Such a system makes services accessible, affordable, delivered through an integrated team approach, and provides good quality services. Overall, the findings suggest that healthy ageing is experienced where a healthcare system that is friendly to older adults is indispensable. The implications of these findings are discussed.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.057
GPT teacher head0.444
Teacher spread0.386 · 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
Published2025
Admission routes2
Has abstractyes

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