Bibliographic record
Abstract
Introduction: Pregnancy anxiety has negative consequences on maternal and fetal health, so today, various interventions have been proposed to control pregnancy anxiety.The aim of this study was to investigate the effect of yoga on the anxiety of pregnant women in their first pregnancy.Methods: A randomized clinical trial was conducted in 2012 on 60 first pregnant women aged 18-40 years old referring to comprehensive health centers of Zahedan.The subjects were randomly divided into intervention and control groups.During the 26-27 weeks of gestation in the intervention group, 24 patients, 15 sessions of yoga and 29 control groups received routine care.Data were collected by a questionnaire for demographic and pregnancy anxiety (PRAQ-R).The pre-test was done at week 26 and the post-test at 34-36 weeks, taking into account the interval of 2 weeks of intervention, and the data were analyzed using paired t-test.The software spss 16 was analyzed.Results: The mean of anxiety score before intervention was not significantly different between the two groups (p = 0.48) but after the intervention, the mean anxiety score decreased in the intervention group (p <0.0001).Statistical tests showed that there was a significant reduction in the level of anxiety after intervention in the intervention group (P = 0.001).There was no significant decrease in the control group (P = 0.11).Conclusion: Yoga exercises are effective in reducing anxiety in pregnant women.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.875 | 0.812 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".