Bibliographic record
Abstract
The last 3 decades of research in exercise science have demonstrated the role of physical activity (PA) in maintaining, as well as improving, a variety of health outcome measures in older adults. However, rates of regular participation in PA remain relatively low. This is a significant public health issue, as inactive and insufficiently physically active older adults are more likely to develop chronic diseases such as heart disease, stroke, and diabetes. Furthermore, disparities in PA exist. For example, older adults in rural areas are less physically active than those in urban areas. Determining why such disparities in health and PA participation exist is a complex, but important endeavor and, presently, the underlying mechanisms are not well understood. By adopting interpretive research methodologies and methods (ie, “qualitative”), we can explore various contextual factors such as historical influences, social norms, and the cultural milieu of particular locations, which may influence area-specific participation in PA. Drawing upon research conducted in Canada, this article discusses the PA perceptions, preferences, and experiences of rural older adults, contextualizes these findings for practice, and considers to what extent a “rethink” of approaches to PA promotion may be necessary to serve future generations of rural older adults.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".