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TRUVACE RECORD VERSION
record: TRV-2026-1557
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-16T10:41:21.208664Z
status: published
lens: p_space
sector: health
headline: Whose Knowledge Counts in Labour? Obstetric Nurses Negotiating Clinical Judgement, Algorithmic Authority and Woman-Centred Care in Artificial Intelligence-Enhanced Foetal Monitoring
dek: Artificial intelligence-enhanced foetal monitoring is entering intrapartum care, yet little is known about how it reshapes the credibility of nursing knowledge, professional authority and relational practice. This interpretive description study explored how obstetric nurses negotiated clinical judgement, autonomy and woman-centred care while working with an artificial intelligence-enhanced cardiotocography system in a physician-led tertiary maternity service in Saudi Arabia. Semi-structured interviews with 18 ob…
gain_title: (none)
problem_title: When nurses' judgement diverged from AI-enhanced cardiotocography output, they faced asymmetric accountability and had to translate embodied, relational knowledge into algorithm-compatible language to gain recognition.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: When nurses' judgement diverged from AI-enhanced cardiotocography output, they faced asymmetric accountability and had to translate embodied, relational knowledge into algorithm-compatible language to gain recognition.
problem_evidence: nurses experienced asymmetric accountability and performed epistemic translation work
quick_read: Researchers interviewed 18 obstetric nurses who had between 6 months and 4 years of experience with an AI-enhanced cardiotocography system in a physician-led tertiary maternity service in Saudi Arabia. Interviews were analysed with reflexive thematic analysis informed by Cognitive Continuum Theory and epistemic injustice.

The findings suggest that AI monitoring in labour is not neutral decision support but changes whose knowledge is treated as credible. That matters for both safety and fairness in maternity care. The evidence comes from one setting and nurses' own accounts, so it cannot show how widely the pattern holds or how it affects clinical outcomes.
limitation: The study is a qualitative interview study of 18 nurses in one physician-led tertiary maternity service in Saudi Arabia, so findings reflect nurses' experiences in a single setting rather than measured clinical outcomes.
tag: Evidence-backed problem
key_points: Nurses read beyond the trace and kept the woman in view while using the system. | Alerts often confirmed concerns nurses had already formed, acting as institutional permission to escalate rather than new clinical information. | Nurses negotiated their autonomy under algorithmic surveillance. | When their judgement diverged from system output, nurses had to recast embodied and relational knowledge in algorithm-compatible language.
rundown: The study used an interpretive description design in a single physician-led tertiary maternity service. It drew on semi-structured interviews with 18 nurses whose experience of the system ranged from 6 months to 4 years.

Nurses described the alerts as lending legitimacy to concerns they already held, which helped them escalate care. When their own assessment conflicted with the system, they carried the accountability burden and had to work to have their knowledge recognised.

The authors frame the technology as an epistemic and relational intervention that can reinforce existing credibility hierarchies, and conclude that whose knowledge counts in labour is a matter of justice as well as safety.
sources:
- peer_reviewed | Nursing Inquiry | https://doi.org/10.1111/nin.70166 | 2026-10-01
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