Exploring the influence of simultaneous motives in organized adult sport and exercise contexts
Bibliographic record
Abstract
Research on physical activity (PA) motivation commonly analyzes the content (Dwyer, 1992) and quality (Ryan & Deci, 2002) of singular reasons for involvement. However, possessing a greater number of personally important motives may also foster commitment (Kruglanski et al., 2013) to PA. This study explored the relationship between the number of personally important motives and measures of PA commitment and lapses, and whether such associations varied depending on whether people were enrolled in exercise/sport contexts. 246 (MAge = 47.20; range = 35-57yrs) self-identified sportspersons (n=105) and exercisers (n=141) completed online questionnaires for PA motives (EMI-2; Markland & Ingledew, 1997), commitment (Scanlan et al., 1993), lapses (Simkin & Gross, 1994), and recent PA (SQUASH; Wendel-Vos et al., 2003). Using EMI-2 responses, and applying within-context normative criteria to each individual, we determined distributions for the number of important motives held by sportspersons and exercisers. Hierarchical regressions examined if participants’ number of motives related to commitment levels, and odds of lapsing, with interactive terms used to determine if these relationships differed between sportspersons and exercisers. Number of motives (B= .27, p < .001) related to commitment (R2= .15, p < .001) equally in sport and exercise, and more strongly than participants’ average past activity involvement (B= .13, p = .009). In a separate regression, number of motives showed no association with lapses (p = .14). Results are discussed as they relate to the value focus tenet of multifinality theory (Kruglanski et al., 2013) and participatory reward structures (Wiltermuth & Gino, 2013).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".