A critical and interpretive literature review of birthing women’s non-elicited pain language
Bibliographic record
Abstract
BACKGROUND: Standardised pain assessment i.e. the McGill Pain Questionnaire provide an elicited pain language. Midwives observe spontaneous non-elicited pain language to guide their assessment of how a woman is coping with labour. This paper examined the labour pain experience using the questions: What type of pain language do women use? Do any of the words match the descriptors of standardised pain assessments? What type of information doverbal and non-verbal cues provide to the midwife? METHODS: A literature search was conducted in 2013. Studies were included if they had pain as the primary outcome and examined non-elicited pain language from the maternal perspective. A total of 12 articles were included. FINDINGS: The analysis revealed six categories in which labour pain can be viewed: 'positive', 'negative', 'physical', 'emotional', 'transcendent' and 'natural'. Women's language comprised i.e. prefixes and suffixes, which indicate the qualities of pain, and figurative language. Language indicated location of pain, gave insight into other life phenomena i.e. death, and shared similarities with standardised pain assessmentdescriptors. Labour cues were 'functional', 'dysfunctional,' or 'neutral' (part of the physiological childbirth process), and were verbal, non-verbal, emotional, psychological, physical behaviour or reactions, or tactile. CONCLUSION: Labour can bring about a spectrum of sensations and therefore emotions from happiness and pleasure to suffering and grief. Spontaneous pain language comprises verbal language and non-verbal behaviour. Narratives are an effective form of pain communication in that they provide details regarding the quality, nature and dimensions of pain, and details notcaptured in quantitative data.
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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 | 0.024 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.027 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".