Effect of ice pack application on pain intensity during active phase of the first stage of labor among primiparaous
Bibliographic record
Abstract
Labor pain management is one of the main goals of maternity care. Ice application or cooling has been claimed to be as an effective, safe and non-invasive adjuvant mean for providing pain relief during the first stage of labor. Aim of the study: To evaluate effect of ice pack application on pain intensity during active phase of the first stage of labor among primiparaous. Research design: None-randomized-controlled clinical trial research design was utilized. Setting: The study was conducted at labor and delivery unit of El Shatby Maternity University Hospital affiliated to Alexandria University. Subjects: Convenience sample of 80 pregnant women attending the previously mentioned setting were recruited in the study. They were equally divided into ice application & control groups. Tools: Three tools were used for data collection, namely: Tool (I): Pregnant women basic data structured interview schedule, Tool II: Visual Analogue pain intensity scale (VAS) & Tool III: Present Behavioral Intensity Scale (PBIS) Tool IV: Satisfaction visual analogue scale (SVAS). A high statistically significant difference was observed between the study & control groups in relation to their pain intensity using VAS before and after the intervention (P ≤ .000). In addition, another high statistically significant difference was detected between the study group & control groups in relation to their behavior of labor pain (P = .000) before & after 30 as well as 60 min of intervention. Based on the study findings, it could be concluded that the application of ice pack application during active phase of first stage of labor appeared to have a remarkable effect on labor pain intensity. In service training programs for nurses in labor units about the utilization of non-pharmacological approaches is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".