Understanding the role of behavior and cognitions in a group exercise setting.
Bibliographic record
Abstract
The first purpose of the present study examined whether individuals with different exercise behaviors (classified by attendance) experienced different or similar cognitive patterns. It was hypothesized that different behavior would lead to different cognitive appraisals. It was predicted that there would be a difference between the three behavioral frequency groups with regard to self-efficacy measures and goal measures. The second purpose of the study was to describe, evaluate and observe whether social factors were associated with participating in exercise in groups. It was hypothesized that those who engage in exercise classes would elicit a social focus. Participants for the study included 39 females who registered in-group fitness classes at a mid-sized university. Attendance over the 10-week course was assessed and participants completed a self-report questionnaire during week seven. The attendance data were used to create 3 exercise frequency groups (regular attenders, sporadic attenders, and dropouts) based on ACSM's exercise guidelines. Analysis of Variance (ANOVA), means and frequencies were used to describe the data. There were no significant differences on measures of self-efficacy, goal measures, enjoyment, and external motivation among the three groups (all p's > 0.05). An analysis of the whole group (N=39) discovered a low social focus and high ratings of self-efficacy. Continued research is necessary to investigate the benefit of social support in a group exercise setting, as well as to better understand how self-regulation through self-efficacy and goal factors influences and is influenced by actual behavior. Key PointsExercise behavior did not seem to differentiate individuals in terms of cognitive factors.Results show low social focus and high self-efficacy in group exercise classes.Continued research is needed to better understand how self-regulation through self-efficacy and goal factors influence and is influenced by exercise behavior.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".