How dynamic are exercise group dynamics? Examining changes in cohesion within class-based exercise programs.
Bibliographic record
Abstract
OBJECTIVE: Within exercise class settings, group cohesion has consistently been found to predict adherence behaviors, and has been identified as a salient target for intervention-based initiatives. Drawing upon theorizing from the field of group dynamics, exercise class cohesion is often conceptualized as a dynamic construct that requires several classes to form and once it is formed, continues to change over time. Despite the salience of this "dynamic" contention for informing physical activity interventions, this theorizing has yet to be empirically tested. METHOD: In this study a multilevel modeling framework was used to examine changes in exercise class cohesion over time. Exercisers (N = 395) completed measures of cohesion following the second, fifth, and eighth classes of their respective programs (N = 46). RESULTS: Mean levels of social cohesion changed significantly over time whereas mean levels of task cohesion did not. These patterns were largely consistent across persons and groups. CONCLUSIONS: These findings suggest that within group-based exercise programs social and task cohesion possesses different levels of dynamism, and that this dynamism (or lack thereof) might have important implications for future research and interventions involving physical activity groups.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".