Bibliographic record
Abstract
Apply It!Summary •Pregnant women are recommended to engage in ≥150 minutes of moderate-intensity physical activity over three or more days per week. •This level of PA significantly reduces the odds of developing pregnancy complications ( e.g. , diabetes and hypertension) without increasing the odds of miscarriage, early delivery, or a small baby. •Engaging in physical activity during pregnancy reduces the odds of developing depression by 67% and reduces the severity of depressive symptoms. •Increasing the amount of weekly exercise concomitantly reduces the odds of developing pregnancy-related complications. As exercise and health professionals, it’s important to consider a woman’s physical, mental, social, and spiritual health when prescribing exercise. Health and fitness professionals can encourage women during this period to be active and to include other areas of wellness by inviting friends to be active with them, or practicing mindfulness during PA, or joining group exercise session for increased social interactions. During pregnancy, there will be days where a woman feels tired or unwell, encouraging adequate rest and sleep to ensure recovery is as important as encouraging a resumption of activity after recovery. Consider the individual’s goal, whether it be disease prevention, stress reduction, or mood enhancement, in the design of an exercise plan or in making recommendations regarding PA. Although prescribing exercise during pregnancy requires additional considerations beyond nonpregnant women, such as time constraints, fatigue, and caring for other children, it is possible to develop effective strategies that increase PA while at the same time improving different aspects of wellness, particularly socioemotional well-being. And all of these can occur with as little as 10 minutes a day of PA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.031 |
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".