Health and recreational motional activity of elderly people in different countries of the world
Bibliographic record
Abstract
The article considers and collects material from both international research and domestic sources that reflect the health and recreational physical activity of the elderly in Europe and the whole world as well as the motives for involvement in this age group. The results of the analysis of the questionnaire, which assessed the involvement of the elderly showed low health and recreational physical activity in different countries, ranging from 10 to 80%, and the activity of men is higher than women. About 40% of the population of the Netherlands and Germany are engaged in physical activity, and as for Belgium, France, Sweden, the indicator is less than 20%. In Spain, Finland, Canada, and the United Kingdom, government programs implemented in 13 pilot regions of the country over four years indicated a threefold increase in the number of seniors who regularly attend group exercise classes. In the United States, the population that is systematically engaged in physical activity is dominated by the population of Euros and Latinos, but African Americans are less active - up to 6%. In African countries physical activity is being decreased among the adult population. The main reasons for that include lack of knowledge of citizens about classes, poor promotion of programs among the population. The survey of the Chinese population on regular exercise during the week of the elderly in the range of 60-69 years showed 11.8%. According to research in Ukraine, only 17% of people of retirement age go in for exercise or sports
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".