Predictors of recreational sport commitment among Ontario seniors games participants: Gender and age effects
Bibliographic record
Abstract
The Sport Commitment Model (SCM; Scanlan et al., 2003) is a framework for understanding the determinants of sport commitment. As no research has applied SCM to older recreational athletes, we studied its predictors among Ontario Senior Games participants, generally, and as a function of gender and age. We surveyed 132 participants (82 m, 50 f; M age = 64.3) from 23 sports. Separate multiple regressions showed that enjoyment (? = .39) and personal investments (.27) predicted functional commitment (FC; R2 = .39), whereas social constraints (? = .45), personal investments (.36), and involvement alternatives (.20) predicted obligatory commitment (OC; R2 = .40). While males had higher involvement alternatives levels, females had higher personal investment levels. A series of regression analyses to examine gender effects showed that enjoyment predicted FC for both, yet personal investments (.32) only explained females' FC. Personal investments predicted OC for both, whereas social constraints (.41) and involvement alternatives (.53) only predicted male and female OC levels, respectively. Regarding age, there were no mean differences for predictors between young (55-64 yr; n = 59) and old (65-78; n = 51) cohorts. Regression analyses showed that personal investments and enjoyment predicted the young group's FC, yet only enjoyment (.57) predicted the old's FC. Personal investments and social constraints explained OC levels for both, but involvement alternatives (.34) predicted only for the young group. We discuss how findings compare with SCM results for younger athletes, and why certain predictors may apply uniquely to older recreational sportspersons.Acknowledgments: SSHRC-Sport Canada Strategic Initiative Grant
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".