Effect of the Sanyinjiao Point (SP6) Acupressure on Reducing Labor Pain at Ismailia Governmental Hospitals
Bibliographic record
Abstract
Background: labor pain is often described as the worst pain in a woman's life, acupressure is one of non-pharmacologic methods can help women copes with the pain of labor. Aim of the study: was to assess the effect of sanyinjiao point (SP6) acupressure on reducing labor pain intensity. Design: a quasi-experimental design was adopted. Setting: data collected from two settings; delivery wards of the Suez Canal University Hospital and General Hospital in Ismailia City. Sample: convenience sample of 60 nulliparous women fulfilled the selection criteria were divided into interventional and control group. Tools of data collection: four tools; Structured Interview, Short form McGill pain questionnaire, Wong-Baker Faces Pain Rating Scale, and Investigation record were used. Results: there was statistical significance reduction of labor pain for study group compared to control group by using Wong Baker faces pain scales immediately, after 30 and 60 minutes from intervention. The mean difference of plasma cortisol level was 1.6±73.7 for interventional group versus 10.37±136.0 for control group with statistical significance differences (p=0.000) immediately after intervention. Conclusions: these findings showed that, SP6 acupressure was effective to decrease labor pain for nulliparous women during active phase of labor. Recommendation: involve application of SP6 acupressure procedure at labor ward as a part of nursing care
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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) | 0.003 | 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".