Examining groupness and satisfaction as predictors of exercise adherence
Bibliographic record
Abstract
Evidence suggests that group constructs are important correlates of exercise behaviour. Perceiving those we exercise with as being more like a "group" appears to be related to our adherence (Spink et al., 2010). Still, why perceptions of groupness relate to adherence has yet to be examined. One possibility may concern one's satisfaction with the group, which is in line with previous research reporting a relationship between groupness and satisfaction (Priebe et al., 2011). Given this finding, it could be proffered that perceiving our co-exercisers as "groupier" results in increased satisfaction with group outcomes, which in turn, promotes better adherence. To test this suggestion, 187 exercisers reflected on a structured exercise group that they had participated in, then responded to an online survey examining groupness (Spink et al., 2010), satisfaction (Bruner & Spink, 2011), and adherence. SEM was used to model a path from groupness to satisfaction, as well as paths from both constructs to adherence. Results indicated that the model fit was acceptable, CFI = .94, RMSEA = .08 (SMC for adherence = .44). While significant paths emerged between groupness and both satisfaction and adherence, no relationship emerged between satisfaction and adherence. These findings highlight the importance of groupness in relation to key exercise outcomes (e.g., satisfaction, adherence), but suggest that satisfaction may not be the best mechanism in the groupness/adherence relationship.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".