Maternal-fetal disease information as a source of exercise motivation during pregnancy.
Bibliographic record
Abstract
OBJECTIVE: A Protection Motivation Theory (PMT) framework was used to examine whether information about the role of exercise in preventing maternal-fetal disease served as a meaningful source of exercise motivation. DESIGN: Pregnant women (n = 208) were randomly assigned into one of three conditions: PMT, attention control, and noncontact control. Women in the PMT group read a brochure about the benefits of exercise during pregnancy incorporating the major components of PMT; perceived vulnerability (PV), perceived severity (PS), response efficacy (RE), and self-efficacy (SE). Participants in the attention-control condition read a brochure about diet. Following treatment, all participants completed measures of their beliefs toward maternal-fetal disease and exercise, goal intention (GI), and implementation intention (IMI). One week later, a measure of self-reported exercise behavior was collected. MAIN OUTCOME MEASURES: Main outcome measures were PMT variables (PV, PS, RE, and SE), GI, IMI, and follow-up physical activity. RESULTS: Participants assigned to the PMT-present group reported significantly higher PS, RE, SE, GI, and increased exercise behavior. PS, RE, and SE predicted GI, GI predicted IMI, and IMI predicted exercise behavior. CONCLUSION: Information grounded in PMT is effective in influencing pregnant women's beliefs and intentions as well as changing their initial behavior.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".