A social-ecological analysis of sport facilitators among older adults
Bibliographic record
Abstract
Purpose/rationale Our research purpose was to examine whether sport facilitators are associated with socio-ecological factors among older adults.Design We used cross-sectional online survey data from 1,207 older adults who were 50 years old or older. Our regression analyses involved personal characteristics (e.g. gender, marital status, and race/ethnicity), past sport behavior, sociocultural (e.g. perceived social coherence), environmental (e.g. rural vs. urban residence), and life course (i.e. age in which people began their sport participation) predictors.Findings All five groups of the social-ecological factors significantly correlated with sport facilitators. Specifically, gender, marital status, histories of individual/dual sport and walking, social coherence, social contribution, social integration, satisfaction with sport programs in communities, and starting sport participation in their 30s or earlier showed robust correlations with sport facilitators.Practical implications We recommended that policymakers target sub-groups of older adults who reported less sport facilitators (e.g. those who are not married or cohabiting) through sport programs with socializing opportunities. 5. Research contribution: We confirmed the socio-ecological nature of sport facilitators, supporting Raymore, 2002 [Facilitators to leisure. Journal of Leisure Research, 34(1), 37–51]. conceptualization of leisure facilitators and Bronfenbrenner, 1977. [Toward an experimental ecology of human development. American Psychologist, 32(7), 513–531] ecological systems model. 6. Originality/value: Our unique focus on the socio-ecological nature of sport facilitators, not sport participation per se, contributed to developing communities that enable active aging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".