Effectiveness of Video-Assisted Demonstration of Postnatal Exercises on Postnatal Well-being among Postnatal Women
Bibliographic record
Abstract
Background: The postnatal period is the time during which women's body adjusts physically to childbearing process and reverts back to its prepregnant state.Exercise after delivery plays a crucial role in improving physical well-being among postnatal women.Objective: To assess the effectiveness of postnatal exercises on postnatal well-being among postnatal women.Material and Methods: Quantitative approach with Quasi-experimental research design (time series design) was used in this study.A total of 60 postnatal women (30 in experimental group and 30 in control group) were selected by convenience sampling technique.Modified Urogenital Distress Inventory and Modified Quebec back pain disability scale were used to assess the postnatal well-being.Videoassisted Demonstration of postnatal exercise was given to experimental group and women performed exercise twice a day (morning and evening) for 3 weeks.The control group received only routine care.Post test was conducted for both groups on 7 th , 14 th and 21 st day.Findings: The study findings showed that there was a statistically significant difference between the experimental and control group at 7 th , 14 th and 21 st day after intervention with regard to Modified Quebec back pain disability scale [p-value = 0.0001, 0.0001 and 0.0001 respectively].In relation to Modified Urogenital Distress Inventory, there was a statistically significant difference between the experimental and control group at 14 th and 21 st day after intervention [p-value = 0.0001 and 0.0001 respectively].Conclusion: This study concludes that video-assisted demonstration of postnatal exercises is an effective method to improve the postnatal well-being among postnatal 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 | 0.003 |
| 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) | 0.003 | 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".