A discourse on healthy ageing in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.050 | 0.026 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".