Aging across the physical activity spectrum: From sedentary behaviour to sport participation
Bibliographic record
Abstract
The following five presentations will provide an update on aging across the physical activity spectrum. Dr. Dogra will present a recently developed consensus statement on sedentary behaviour in older men and women. This consensus was based on a systematic review of the literature, a 3 stage Delphi consensus process with international experts in the field, and an in-person meeting with experts in July of 2016. Dr. Weir will present data from focus groups conducted with socially engaged older adults (n=26). Two main themes that fell into each of the four domains of the ecological model of sedentary behaviour emerged. These themes were barriers and promoters of sedentary behaviour, and were either intrinsic or extrinsic. Dr. Gayman will present results from an analysis of the Canadian General Social Survey (2010) investigating the influence of participation in low effort activities (i.e., MET < 1.5). Results indicate that athletes are less sedentary than leisurely active or inactive older adults (n= 1,723). Dr. Dogra will present results from a similar study comparing sedentary time between master and recreational athletes; however, in contrast to Dr. Gayman's results, these results indicate that Masters athletes may be compensating for vigorous exercise with an increase in sedentary time. Finally, Dr. Horton will present results from a qualitative investigation of older women competing in the 2013 World Masters Games. Three main themes emerged from the interviews (n=16): Multi-faceted benefits, Overcoming barriers, and Social roles. By resisting gender and aging stereotypes, women may help to change perceptions of aging.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".