What can Sub-Saharan Africa learn from Canada’s investment in active healthy ageing? A narrative view
Bibliographic record
Abstract
Background: The number of older persons in Sub-Saharan Africa is increasing. Aims: What can Sub-Saharan Africa learn from other countries that may enhance the health and wellness of older persons? Canada was conveniently selected as the country which has endorsed the need for action on active ageing, given that by 2026, 1 in every 5 Canadians will have reached the age of 65 years and 4% of the overall population will be 85 years and older. Methods: English language electronic searches of computerized databases (PubMed, MEDLINE, EMBASE, CINAHL, and PsychINFO) were done to locate relevant published studies on Canada, from January 2000 to August 2014. Keyword combination included: physical activity/activities, exercise/s, older person/s, elderly, ageing adults, seniors, and older people. Results: 8 out of 400 plus articles were reviewed, and 4 key approaches in ensuring active ageing in Canada were identified. From these, 5 public health-oriented plans are recommended for Sub-Saharan Africa: (1) there should be a shift in the conceptualisation of what physical activity entails, (2) it is necessary to build and strengthen collaboration between various stakeholders involved in planning, (3) raising awareness among older persons and the general population on the benefits in participating in physical activity, (4) encourage older persons to participate in culturally relevant physical activity, and (5) laying a better foundation for future generations of older persons. Conclusion: Though more elaborate planning is required, these recommended plans will contribute to achieving average life expectancy beyond 60 years in Sub-Saharan Africa.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".