Pain assessment in labouring women using self-report tools: a scoping review
Bibliographic record
Abstract
Background Childbirth progress is much related to labour pain, whereby the progress can be predicted using suitable pain assessment tools. Objective To summarise methodology used for the assessment includes scales, questionnaires, tests, and other methods used to assess pain severity and childbirth progress in labouring women. Search strategy Elsevier® Scopus and MEDLINE (Medical Literature Analysis and Retrieval System Online, or MEDLARS Online accessed using PubMed®) were systematically searched in November 2021. Selection criteria Original research utilising pain assessment tools to assess pain severity and labour progress. Data collection and analysis Data on study characteristics, labour pain assessment tools, and labour evaluation are qualitatively synthesised. Main results There are various types of pain assessment tools that were identified. Nineteen (19) papers used the Visual Analogue Scale to measure the pain in labouring women, 14 studies used McGill Pain Questionnaire, and one (1) study used the questionnaire in the Danish version, while seven (7) papers used numerical rating scale in their studies to assess the labour pain in labouring women. For the progression of labour, ten (10) research papers assess the labour progress by examining cervical dilation and two (2) papers address the duration of labour as the measurement in their studies. Conclusions Many pains assessment tools are available to measure pain in labouring women. Most articles used the Visual Analogue Scale to assess childbirth pain with cervical dilatation to assess the labour progress. However, there is still limited information available in the literature about suitable pain assessment to predict labour progress.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".