Self-Efficacy and Perceived Exertion of Girls During Exercise
Bibliographic record
Abstract
BACKGROUND: An important national goal in Healthy People 2010 is to reduce the high prevalence of sedentary lifestyles and resultant overweight and obesity among girls. OBJECTIVES: The purpose of the present study was threefold: (a) to determine if pre-exercise self-efficacy predicted girls' perceptions of exertion during exercise, (b) to determine if these perceptions, in turn, influenced postexercise self-efficacy, and (c) to assess if exercise self-efficacy increased following completion of an exercise task. METHODS: A sample of 103 girls, 8 to 17 years of age, pedaled 20 minutes on a cycle ergometer at 60% of their predetermined peak VO2 in a climatic chamber (90 degrees F, 50% relative humidity). Ratings of perceived exertion were obtained every 4 minutes. Exercise self-efficacy was assessed before and after the exercise session. RESULTS: Controlling for peak VO2 and percent body fat, pre-exercise efficacy exerted an independent effect on perception of exertion during exercise with girls high on pre-exercise self-efficacy reporting lower perceived exertion during exercise, than girls low on self-efficacy. Both pre-exercise efficacy and perceived exertion explained postexercise efficacy. Exercise self-efficacy increased significantly from pre- to postexercise. CONCLUSIONS: Pre-exercise efficacy is an important factor influencing girls' perceptions of exertion during exercise and their postexercise efficacy. Increased exercise self-efficacy of girls following successful completion of an exercise challenge suggests possible strategies to increase physical activity.
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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 | 0.000 | 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) | 0.003 | 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".