Coping self-efficacy mediates the influence of generalized control beliefs on physical activity behavior and intentions to be active: A population based sample
Bibliographic record
Abstract
Generalized beliefs about one’s control are thought to affect behaviors and behavioral intentions (BI). We sought to examine how a sense of mastery and constraints contribute to a specific control belief, exercise self-efficacy (SE), as well as BI and physical activity (PA) behavior in the Alberta population. We thought SE would partially mediate the effect of mastery and constraints on exercise and BI, with constraints showing a direct influence on PA. A random digit dialing survey of 1210 Albertans was conducted in Calgary, Edmonton and elsewhere. Participants were asked about control beliefs (mastery and constraints) and SE for coping with barriers to PA as well as their PA behavior and their BI to engage in PA. Structural equation modeling was used to examine the role of mastery and constraints on SE and BI and then on behavior. Mastery was positively related to SE and those reporting higher constraints were less confident in the ability to cope with the demands of exercise. Coping SE fully mediated the relationship between mastery and constraints and behavior, as well as BI. Standardized direct effects between SE and PA behavior and BI were .47 and .21, respectively. General beliefs people hold about the extent to which they can control their lives influences specific beliefs, such as coping self-efficacy. Perceiving broader barriers in one’s life lead to less confidence in one’s ability to cope with the demands of regular PA, and consequently less PA. Broader beliefs about one’s control may serve as a target to influence behavioral specific control beliefs such as coping SE for exercise, in future interventions to improve SE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".