What Informs Self-efficacy To Be Active During Pregnancy?
Bibliographic record
Abstract
Self-efficacy facilitates adherence to prenatal exercise which elicits health benefits for the pregnant person and developing fetus. PURPOSE: The purpose of this study was to characterize self-efficacy for exercise during pregnancy and identify theoretically derived antecedents that contribute to high self-efficacy profiles. METHODS: A cross-sectional survey was conducted using RedCap. Eligible participants were currently pregnant, ≥18 years old, did not have contraindications to exercise, and could communicate in English. Participants completed the Pregnancy Exercise Self Efficacy Scale, and were grouped as high, moderate or low self-efficacy. They indicated the degree to which factors that influence self-efficacy (i.e., mastery experience, verbal persuasion, physiological feedback, social persuasion) affected their exercise participation. Open-ended responses described motivators and barriers for exercise. One-Way ANOVA and Chi-Square analyses compared self-efficacy factors. A conventional content analysis was performed on open-ended responses and compared by Chi-Square analyses. RESULTS: Seventy-two participants were included and grouped according to high (n = 18), moderate (n = 50) and low (n = 4) self-efficacy. Mastery experiences and physiological affect were ranked as most influential by the high-self efficacy group compared to both moderate (p = 0.001) and low (p < 0.001) self-efficacy groups. The main reasons for being active were for improving the health of the baby and the pregnant person. The main barriers to being active were fatigue and lack of time. There were no differences between groups for frequency of reporting motivators or barriers. CONCLUSIONS: Mastery experiences (e.g., previous activity levels) and physiological affect (e.g., improved mood post-exercise) promote high self-efficacy for prenatal exercise. Future interventions may use these findings to facilitate prenatal exercise adherence by leveraging factors that are likely to enhance self-efficacy.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".