A longitudinal study of masters swimmers' commitment
Bibliographic record
Abstract
The Sport Commitment Model (SCM; Scanlan et al., 2003) is a framework for understanding the determinants of commitment to sport participation. Using a modified version of the SCM (Wilson et al., 2004), a longitudinal survey of 190 international-level Masters swimmers (91 m, 99 f;M age = 51.9 yrs; range = 26–80) was conducted on two occasions, one year apart. In Analysis 1, groups were designated based on change scores for determinant constructs over the year. A series of repeated measures ANOVAs revealed significant interactive effects between determinant groups and time for functional commitment (FC), but not for obligatory commitment (OC). Post-hoc paired sample t-tests showed that groups increasing in enjoyment and satisfaction, personal investment, and involvement opportunities had increased FC, while groups decreasing in enjoyment and satisfaction had decreased FC (all ps < .01). In Analysis 2, simultaneous multiple regression analyses indicated that changes in FC (R2 = .34, p < .001) were significantly predicted by changes in enjoyment and satisfaction (? = .46) and personal investment (.22), while changes in OC (R2 = .18, p < .001) were significantly predicted by personal investment (.26) and involvement alternatives (.17) and inversely predicted by social support (-.27) (all ps < .05). Results provide support for the efficacy of the SCM in contributing to an understanding of the dynamic nature of sport commitment.Acknowledgments: This research was supported by a 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".