ArticleNursing science quarterly2026
From Joint Cognitive Systems to Human-AI Joint Cognitive Systems: A Theory Critique and Application to Obstetric Anesthesia Risk Assessment.
Article in Nursing science quarterly, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- The Cognitive Transaction: Toward a Human Factors Research Agenda for AI in Anesthesia and Perioperative Care.JMIR human factors · 2026Article
- Artificial Intelligence for Perioperative Risk Prediction in Anesthesiology: A Bibliometric and Knowledge-Mapping Analysis.Healthcare (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This article examines the joint cognitive systems framework as a foundation for understanding human-machine collaboration. Using Meleis's theory evaluation model, we describe its origins, principles, and applications and then critique its strengths and limitations. Findings highlight challenges in trust, workload, and integration that limit effective use in health care. Application to obstetric anesthesia demonstrates how combining provider expertise with machine learning insights may improve maternal risk assessment. We propose the modernized extension,
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.