A Comparison of Massage Effects on Labor Pain Using the McGill Pain Questionnaire
Bibliographic record
Abstract
The purpose of this study was to describe the characteristics of pain during labor with and without massage. Sixty primiparas in labor were randomly assigned to either a massage or control group and tested using the self-reported Short-Form McGill Pain Questionnaire (SF-MPQ) at 3 phases of cervical dilation: phase 1 dilation (3-4 cm), phase 2 dilation (5-7 cm), and phase 3 dilation (8-10 cm). The massage group received standard nursing care and massage intervention, whereas the control group received standard nursing care only. The results of this study showed: (1) In both groups, as cervical dilation increased, there were significant increases in pain intensity as measured by SF-MPQ; (2) massage lessened pain intensity at phase 1 and phase 2, but there were no significant differences between the groups at phase 3; (3) the most frequently selected five sensory words chosen by both groups were similar at phases 1 and 2- (a) sore, (b) sharp, (c) heavy, (d) throbbing, and (e) cramping, while of the 4 affective classes, "fearful" and "tiring-exhausting" were the most used by participants to describe the affective dimension. The results of this study indicate that, although massage cannot change the characteristics of pain experienced by women in labor, it can effectively decrease labor pain intensity at phase 1 and phase 2 of cervical dilation during labor. Nurses and caregivers could consider using massage to help laboring women through the labor pain.
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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".