Exploring older women's experiences of sport participation
Bibliographic record
Abstract
In the summer of 2015, Canada crossed a key demographic threshold, as there are now more of its citizens 65 years and older than under 15. This mirrors the demographic trends globally; by 2050 the number of adults over the age of 60 in developed countries will nearly double those under 15. Encouraging sport participation is one method governments have utilized in the attempt to facilitate a more active senior citizenry. Regardless of whether governmental influence is actually having an effect on participation rates, it is clear that sporting events for older adults are growing in popularity. A notable example of this is the World Masters Games (WMG) which began in 1985 with 5,000 athletes; it is now the largest sporting event in the world with 30,000 participating in the quadrennial event. To date, investigations of seniors' participation in sport has focused primarily on physiological variables, with fewer investigations devoted to psycho-social outcomes. Of these, only two have examined older women's experience in sport. This study attempted to address this shortfall with a qualitative investigation of older women competing in the 2013 WMG. Interviews were conducted with 16 women ranging from 70 to 86 years of age. Three main themes emerged from the analysis: Multi-faceted benefits, Overcoming barriers, and Social roles. There is unquestionably complexity inherent to older females' sport participation, however, by resisting gender and aging stereotypes the women in our study and others like them may help to change perceptions of what it means to grow old.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".