Group Dynamics in Physical Activity Promotion: What works?
Bibliographic record
Abstract
Abstract Over the past 20 years group dynamics‐based interventions have been used to successfully increase physical activity. However, the literature is less clear on the underlying mechanisms of effectiveness. That is, what makes these group dynamics interventions work? We conducted a systematic review to identify studies that used different group dynamics strategies to promote physical activity. Seventeen studies were identified and were coded by two raters to determine the degree to which group dynamics strategies were used, the format of the programs, and any analytic procedures used to determine the causal mechanisms underlying intervention effectiveness. The results of the coding indicated that while there is no standard package of group dynamics strategies being applied across the literature‐ and regardless of the breadth of the underlying theory or the structure of the programs‐ the effect on physical activity is robust. However, few studies explicitly measured potential causal mechanisms and even fewer completed the necessary analysis to detect mediation. We concluded that future research on developing a unified theory for group dynamics with appropriate measurement tools is necessary to further enhance the effects of group dynamics on physical activity promotion.
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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 | 0.032 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".