A research framework for the United Nations Decade of Healthy Ageing (2021–2030)
Bibliographic record
Abstract
The mission of UN Decade of Healthy Ageing (2020-2030) is to improve the lives of older people, their families and their communities. In this paper, we create a conceptual framework and research agenda for researchers to knowledge to address the Decade action items. The framework builds on the main components of healthy ageing: Environments (highlighting society and community) across life courses (of work and family) toward wellbeing (of individuals, family members and communities). Knowledge gaps are identified within each area as priority research actions. Within societal environments, interrogating beliefs about ageism and about familism are proposed as a way to illustrate how macro approaches to older people influence their experiences. We need to interrogate the extent to which communities are good places to grow old; and whether they have sufficient resources to be supportive to older residents. Further articulation of trajectories and turning points across the full span of work and of family life courses is proposed to better understand their diversities and the extent to which they lead to adequate financial and social resources in later life. Components of wellbeing are proposed to monitor improvement in the lives of older people, their families and communities. Researcher priorities can be informed by regional and national strategies reflecting Decade actions.
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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 | 0.095 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".