Navigating Quality and Innovation: Actor‐Network Theory and Hybrid Assemblages in Midwifery Practice, Implications of Maternity Early Warning Tools and Artificial Intelligence
Bibliographic record
Abstract
Midwifery philosophy views childbearing as primarily normal, indicative of a woman's overall health. Midwifery practice focuses on supporting the human-to-human relationship between the midwife and the woman holding primacy. Despite the traditional focus on wellness, maternity care in today's risk averse world is increasingly complex. Technology has been increasingly implemented into maternity care to detect complications early and reduce harm. The Maternity Early Warning Tool is a technological innovation in this regard. Actor-network theory (ANT) offers a framework for analysing the connections between human actors (women, fetuses, and midwives) and nonhuman actors (machines, tools, and policies) within healthcare. This paper through drawing on the tenets of ANT, particularly in understanding the adoption of Maternity Early Warning Tools in midwifery practice, examines and explores the implications of integrating these tools in relation to midwifery practice. ANT also guides thoughtful considerations regarding the potential trajectory of Artificial Intelligence in midwifery, specifically regarding how these technological advancements alter midwifery practice by creating new hybrid assemblages and fluid identities. This discussion of subversive elements enhances understanding of the implications of Maternity Early Warning Tools on contemporary midwifery practice.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".