Exploring whether participatory motives relate to commitment and lapses differently in adult sportspersons and exercisers
Bibliographic record
Abstract
Structured physical activity (PA) programs that accommodate personal motivational preferences can promote sustained participation (Weiss & Amorose, 2008). Thus, it is important to explore whether sport and exercise programs cater to different motivational profiles (Kilpatrick et al., 2005). This study examined how various participatory motives for PA were differentially associated with commitment and lapses in sportspersons and exercisers. 252 (MAge=47.20; range=35-57yrs) self-identified sportspersons (n=108) and exercisers (n=144) completed online questionnaires to report their PA motives (EMI-2; Markland & Ingledew, 1997), commitment levels (Scanlan et al., 1993) and frequency of lapses (Simkin & Gross, 1994). Exploratory factor analyses determined important motives in each of sport and exercise contexts. A series of simultaneous regressions examined associations between these motives and commitment/lapses, and hierarchical regressions employed interactive terms to determine whether such associations differed as a function of PA context. Enjoyment and health motives were highly important for sportspersons and exercisers alike, whereas competition and social affiliation were more important to sportspersons. Enjoyment (B=.20, p=.009), social affiliation (B=.13, p=.05), and stress relief (B=.12, p=.08) predicted commitment for sportspersons and exercisers alike. Sportspersons’ commitment was also uniquely related to appearance (B=-.26, p=.006) and personal goals and challenges (B=.17, p=.06) motives. Stress relief motives buffered against lapsing (B=-.61, p=.05) for both sportspersons and exercisers, whereas health related motives only buffered against lapsing for exercisers, yet were positively associated with lapsing for sportspersons. Discussion focuses on how programmers might consider between-context differences when programming organized PA to better accommodate and motivate adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".