Development of a web-based “Perineal Care Protocol” educational model as assistance for postpartum perineal wound care at home
Bibliographic record
Abstract
search, G -Funds CollectionBackground. 70% of cases where there is a tear in the perineal tissue at the time of delivery, either spontaneously or with an episiotomy, require perineal suture treatment.Postpartum mothers who experience a delivery with an episiotomy indicate that they experience a higher level of pain.Objectives.The educational model "Perineal Self Care Protocol" is an educational intervention model with a web-based application method for postpartum mothers at home.Material and methods.This research was conducted in the working area of the Medan Sunggal and Medan Amplas health centres with a sample size of 138 women who were divided into 2 groups, namely the intervention group (the Medan Sunggal working area) and the control group (the Medan Amplas work area).A sampling technique was carried out by purposive sampling with the aim of identifying self-efficacy assessment is done by "Perineal Self Care Protocol" education module for 4 consecutive days. Results.In the results of this research, there was an increase in postpartum mother's self-efficacy with a significance value of p < 0.005, where 10 items of self-efficacy that have been shown to increase with the development of education through the website have been found to increase.Conclusions.In the results of this research, there was an increase in postpartum mother's self-efficacy with a significance value of p < 0.005, where there were 10 items of self-efficacy components that were proven to increase with the development of education through the website.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".