Women's experiences of speaking up for safety during pregnancy, labour and birth
Bibliographic record
Abstract
Background The contribution of patients to their own safety is receiving increased attention, and there is some evidence that patients detect some suspected adverse events earlier than professionals. However, little is known about maternity care. We draw on data from two component studies from Birthplace in England and NIHR King's Patient Safety Research Programme. Both explored the context, experience and impact of women speaking up, which we define as ‘making assertive and insistent attempts to communicate concerns or safety alerts to staff’. Methods Organisational case studies of 5 NHS Trusts in 4 health regions in England. Data collected from March 2010 to December 2011 included: observation of meetings and ward life (>200 hours); semi-structured interviews with staff, managers and stakeholders including user-group representatives (n=130) and postnatal women and birth partners (n=92). Data was analysed by team triangulation using NVivo8 software. Results Half the women “spoke up” and a quarter gave safety alerts about issues they considered urgent including reduced fetal movements, signs of risk in labour, abnormal pain, feeling unsafe, and neonatal or postnatal pathologies. Women from a range of socio-demographic backgrounds were more able to speak up in the supportive presence of a partner or relative. Women reported that staff failure to listen had affected some clinical outcomes, their sense of safety, and trust in the system. Conclusion Speaking up, when heard and responded to, can contribute to safety and improve women's overall experience of care. Ignoring women's concerns and safety alerts may lead to avoidable harm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".