Tools for assessing labour pain: a comprehensive review of research literature
Bibliographic record
Abstract
ABSTRACT: The experience of pain associated with labour is complex and challenging to assess. A range of pain measurement tools are reported in the literature. This review aimed to identify current tools used in research to assess labour pain across the past decade and to evaluate their implementation and adequacy when used in the context of labour pain. A literature search was conducted in databases MEDLINE and Cumulative Index of Nursing and Allied Health Literature, using search terms relating to labour, pain, and measurement. A total of 363 articles were selected for inclusion. Most studies (89.9%) assessed pain as a unidimensional experience, with the most common tool being the Visual Analogue Scale, followed by the Numerical Rating Scale. Where studies assessed pain as a multidimensional experience, the most common measurement tool was the McGill Pain Questionnaire. Only 4 studies that used multidimensional tools selected a tool that was capable of capturing positive affective states. Numerous variations in the implementation of scales were noted. This included 35 variations found in the wording of the upper and lower anchors of the Visual Analogue Scale, some assessment tools not allowing an option for "no pain," and instances where only sections of validated tools were used. It is clear that development of a standardised pain assessment strategy, which evaluates the multidimensions of labour pain efficiently and effectively and allows for both positive and negative experiences of pain to be reported, is needed.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".