"The more we get together": Groupness and adherence in structured and unstructured activity settings
Bibliographic record
Abstract
When exercising with others, it has been reported that the more one perceives a collection of others to be a group (i.e., groupness), the greater one's adherence in that group setting (Spink et al., 2010). This relationship between groupness and adherence has been reported only in structured exercise settings. Given that individuals also report exercising with others in unstructured settings (e.g., running with friends) and different psychosocial correlates have been associated with adherence in these two settings (Spink et al., 2006), one might wonder if the effects of groupness differ across the settings. The purpose of this study was to examine the relationship between groupness and adherence in structured and unstructured activity settings. Participants from both structured (N=150) and unstructured (N=153) settings completed online questionnaires that assessed groupness (Spink et al., 2010) and adherence (attendance and frequency). SEM was used to evaluate whether groupness would predict adherence. In the structured setting, a good model fit was found: X2= 5.82, p=0.67, RMSEA=0.00 (CI: 0.00-0.08) with the squared multiple correlation (SMC) for adherence = 0.57. A good model fit also was found in the unstructured setting: X2= 10.08, p=0.26, RMSEA=0.04 (CI: 0.00-0.11). However, the SMC of 0.001 was marginal. The findings support the role of groupness in predicting adherence and suggest possible differences between structured and unstructured activity settings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".