Perceived impact of physical activity on health and wellness of older adults in Northern British Columbia.
Bibliographic record
Abstract
This research rests on earlier research suggesting that there is a definitive connection between physical activity and the health and wellness of older adults. Aspects of this connection were examined through a qualitative research project with a sample of older adults in Prince George, the largest city in northern British Columbia, Canada. The research explored the experiences and perspectives of older adults about the impact of physical activity on their health and wellbeing. Using a purposeful sampling method, data was generated through focus group and in-depth interviews. The data generated was analyzed using thematic analysis. The following eight themes emerged from the data analyzed: (1) Enthusiasm to learn more about and be involved in physical activity, (2) Effects of northern climate on involvement in physical activity, (3) Prominent physical activity, (4) Impact of physical illness, (5) Reason for being involved in physical activity, (6) Reasons for not being involved in physical activity, (7) Physical activity contributes to good health, and (8) Other views on physical activity in the community. The findings of this research are expected to benefit older adults, their families, and Northern Health and its agencies / programs involved in delivering services to older adults in Prince George and neighboring towns. --Leaf ii.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".