Task, Coping, and Scheduling Self‐Efficacy in Relation to Frequency of Physical Activity<sup>1</sup>
Bibliographic record
Abstract
Self‐efficacy has been shown to be a robust predictor of exercise and other health‐related behaviors (e.g., Bandura, 1986, 1995, 1997; Godin, Desharnais, Valois, & Bradet, 1995; Maddux, 1995; McAuley, Wraith, & Duncan, 1991). Maddux has proposed that there are different types of self‐efficacy and that these types may fulfill different roles in the motivation of behavior, perhaps based on characteristics of the target behavior. The purpose of this study was to examine 3 different types of self‐efficacy: task, coping, and scheduling and their respective usefulness in distinguishing among persons reporting different levels of exercise involvement. A cross‐sectional telephone survey using exercise behavior as the selection criterion was completed with 203 adults. Results showed that coping and scheduling efficacy were the best disciminators of level of exercise behavior. Task efficacy did not clearly distinguish between exercise groups. The theoretical and applied implications are discussed, particularly noting specific targets for future intervention.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".