Bibliographic record
Abstract
UNLABELLED: The purpose of this study was to investigate determinants of the activity patterns of women prior to pregnancy and factors associated with quitting activities during pregnancy. METHODS: These data arose from a study designed to look at the impact of exercise in pregnancy on birth weight (Campbell and Mottola, 2001). This secondary analysis explored relationships between subject characteristics and exercise patterns via a self-completed questionnaire. Univariable and multivariable odds ratios were estimated using logistic regression. Multivariable models used backward stepwise variable selection. RESULTS: A total of 853 women agreed to participate and 529 women (62%) returned completed questionnaires. Of these, 369 (70%) and 258 (49%) engaged in a structured exercise program before pregnancy and in Trimester 3, respectively. Factors associated with engaging in regular structured exercise prior to pregnancy included: postsecondary education (OR = 1.50; 0.98, 2.30), no children (OR = 2.44; 1.56, 3.82), nonsmoker (OR = 1.84; 1.18, 2.88), and involvement in regular recreational activities (OR = 3.07; 1.81, 5.20). During pregnancy, all categories of activity decreased except walking, which increased by Trimester 3. Factors associated with quitting a regular structured exercise program by Trimester 3 were: having children (OR = 1.67; 1.05, 2.67), a prepregnancy BMI of 25 (OR = 1.79; 1.04, 3.13), and higher weight gain. IMPLICATIONS: Community programs that encourage active living should address these factors.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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 | 0.001 |
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".