Perceptions of labour pain management of Dutch primary care midwives: a focus group study
Bibliographic record
Abstract
BACKGROUND: Labour pain is a major concern for women, their partners and maternity health care professionals. However, little is known about Dutch midwives' perceptions of working with women experiencing labour pain. The aim of this study was to explore midwives' perceptions of supporting women in dealing with pain during labour. METHODS: We conducted a qualitative focus group study with four focus groups, including a total of 23 midwives from 23 midwifery practices across the country. Purposive sampling was used to select the practices. The constant comparison method of Glaser and Straus (1967, ren. 1995) was used to gain an understanding of midwives' perceptions regarding labour pain management. RESULTS: We found two main themes. The first theme concerned the midwives' experienced professional role conflict, which was reflected in their approach of labour pain management along a spectrum from "working with pain" to a "pain relief" approach. The second theme identified situational factors, including time constraints; discontinuity of care; role of the partner; and various cultural influences, that altered the context in which care was provided and how midwives saw their professional role. CONCLUSION: Midwives felt challenged by the need to balance their professional attitude towards normal birth and labour pain, which favours working with pain, with the shift in society towards a wider acceptance of pharmacological pain management during labour. This shift compelled them to redefine their professional identity.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".