Exercise Parameters and Postpartum Outcomes Among Pregnant Women
Bibliographic record
Abstract
Childbirth has always been a critical experience, resulting to physiological changes and imputing physical challenges, such as extreme pain, to a woman. This is why obstetrical inquiries look into factors and ways to make labor pains less dreadful, improve delivery outcome and hasten recovery time. Thus, this study aims to determine the relationship between exercise parameters and the postpartum outcomes among pregnant women in Cagayan de Oro City. This study utilizes an observational cross-sectional method involving 116 pregnant women chosen through convenient sampling method in selected barangays of Cagayan de Oro City. A 5-part questionnaire based from the literature on the parameters of exercise and an adopted questionnaire was used from McGill Pain Questionnaire. Results show that for the demographic characteristics of pregnant women, BMI affects level of pain perception; while age affects both pain perception and delivery outcome; and parity is associated with the occurrence of complications. For the exercise parameters, the type of exercise affects body weight, and frequency of exercise during pregnancy influences type of delivery. Thus, regular exercise program during the course of pregnancy and having ideal demographic characteristics results to shorter and lesser labor pains. Keywords : Pregnancy, Childbirth, Exercise Parameters, Postpartum outcomes
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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 | 0.001 |
| 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) | 0.001 | 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".