Predicting Scheduling Self-Efficacy in Older Adult Exercisers: The Role of Task Cohesion
Bibliographic record
Abstract
The study examined the relative influence of 2 forms of task cohesion on older adult exercisers’ (N = 82) self-efficacy to schedule exercise into their weekly routine. Participants had been involved with the exercise program for at least 4 months before the study began. A sequencing protocol was used to allow for task cohesion’s influence on scheduling self-efficacy. Task cohesion, as measured by the Group Environment Questionnaire, was assessed during the 1st week of exercise classes after a holiday. Scheduling self-efficacy was assessed at midprogram. Attractions to the group-task and group-integration-task cohesion were sequentially entered into a hierarchical regression analysis while recent attendance was controlled for. Results showed individual attractions to the group task accounted for most of the variance in scheduling self-efficacy. R2 = .10, F(2,80) = 4.22,p = .02; the addition of group-integration task also significantly (p < .05) added variance. R2 = .13. F(3, 79) = 3.79, p = .01.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".