Examining group cohesion and adherence in unstructured exercise settings
Bibliographic record
Abstract
While cohesion has been identified as an important predictor of individual exercise adherence in group settings (Spink & Carron, 1993), to date, this relationship has only been established in structured exercise programs. It is worth recognizing that many individuals are often active in groups that are less structured (e.g., workouts with friends). As distinctions have been reported in the psychosocial correlates most associated with exercise in structured and unstructured exercise settings (Spink et al., in press), this study aimed to examine the relationship between cohesion and adherence in an unstructured group exercise setting. Participants (N = 104) who reported exercising with others in an unstructured setting in the previous 4 weeks indicated the frequency of their exercise in this setting and completed the modified GEQ (Carron et al., 1985) to assess cohesion within that group. Regression results revealed two cohesion dimensions predicted exercise frequency: GI-Task, ? = .38, t (99) = 2.31, p < .05, and GI-Social, ? = -.30, t (99) = -1.99, p < .05. In contrast to previous results in structured settings, the GI dimensions of cohesion emerged as predictors of attendance behaviour, suggesting possible differences between structured and unstructured exercise settings. Also, the fact that social cohesion was found to be negatively related to adherence contrasts with previous findings that have suggested that cohesion is typically positively related to adherence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".