Does how we think about others matter? Examining similarity and groupness in relation to exercise adherence
Bibliographic record
Abstract
Perceptions about those we exercise with appear to impact individual adherence. For instance, the perceived similarity of our co-exercisers has been associated with increased exercise participation (Dunlop & Beauchamp, 2011). As well, the extent to which we perceive these people to be a group (i.e., groupness) has been positively related to adherence (Spink et al., 2010). Although Ulvick et al. (2012) reported a relationship between deep-level similarity (DLS) and groupness, it is unclear whether these constructs operate together to predict adherence. The present study aimed to concurrently examine the relationships between DLS, groupness, and adherence. Adults (N = 185), recalling a structured exercise group they had participated in, completed online measures of DLS (Harrison et al., 1998), groupness (Spink et al., 2010), and adherence (frequency and percent attendance). SEM was used to examine DLS and groupness as predictors of adherence, as well as the relationship between DLS and groupness. Results indicated an adequate model fit, CFI = .94, RMSEA = .08, with a SMC for adherence of .46. The relationships between DLS and adherence, groupness and adherence, and DLS and groupness were all positive and significant. One interpretation might be that perceptions of similarity contribute to groupness, and perceptions of groupness then lead to increased adherence. Investigating groupness as a possible mediator in the similarity-adherence relationship awaits future research.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".