Perceptions of labour pain by mothers and their attending midwives
Bibliographic record
Abstract
AIMS: The aim of the current study was to examine the perception of pain by labouring women and their attendant midwife, from the onset of labour to delivery. RATIONALE: Accurate measurement and appropriate management of pain is a significant problem for attendant medical and nursing personnel. Both the experience and perception of pain are regarded as subjective and are therefore difficult to measure objectively. Indeed, much of the literature reports that pain is often under- or over-estimated by nursing staff who as a consequence consistently fail to administer adequate analgesia. Few studies have specifically examined the ability of midwives to assess the pain of labouring women. DESIGN: The short form McGill Pain Questionnaire (SF-MPQ), routinely used to assess pain in obstetric environments, was used to determine pain perception. Thirteen labouring women and nine midwives completed the SF-MPQ every 15 minutes beginning at the time of admittance to the delivery suite. Peak pain ratings for the preceding 15 minutes were obtained without reference to prior ratings or each other's scores. Further, midwives in the maternity unit of The Queen Elizabeth Hospital (TQEH), Adelaide, South Australia completed a survey investigating the cues they use to assess pain during labour. RESULTS: On each measure of pain on the SF-MPQ, the midwives scores correlated with the mothers' scores across the entire pain range. Further analysis showed that mothers' and midwives' pain scores were similar at mild-moderate pain levels, but midwives significantly underestimated pain intensity at levels that mothers described as severe. The survey responses indicated that midwives rely on both verbal and nonverbal cues to assess pain levels. CONCLUSIONS: The cues used by midwives to differentiate pain intensities and qualities are similar to those used in other clinical settings, but may have limited discriminatory value as pain levels become severe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".