ArticleBMC nursing2026
Training effect of a deep learning-based blended teaching model on ECMO transport for ICU nurses: a prospective, parallel-group, randomized controlled trial.
Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveGiven the high alignment between deep learning and blended teaching objectives, blended teaching provides a feasible pathway for achieving deep learning. This study aimed to verify the efficacy of a deep learning-based blended teaching model integrating online immersive learning, offline scenario simulation and clinical case review in improving ECMO transport competency among ICU nurses.
methodsThis was a single-center prospective parallel-group randomized controlled trial. A total of 130 ICU nurses were randomly assigned to experimental group (n = 68) and control group (n = 62). The experimental group received the blended teaching intervention, while the control group adopted traditional offline lecture teaching. Baseline questionnaires, learning style and motivation scales were collected before intervention. Eye-tracking technology and engagement scales were used to evaluate learning immersion during training. Post-training knowledge level and satisfaction were assessed, and paired t-tests were adopted for within-group comparison.
resultsBaseline data were comparable between the two groups. The experimental group showed significantly better eye-tracking immersion indicators and higher online learning participation than the control group (all P < 0.001). Post-training knowledge scores were significantly improved in the experimental group, while no obvious change was found in the control group. The intervention presented a small positive effect size. No significant between-group differences were observed in learning engagement, cognitive participation and satisfaction.
conclusionCompared with traditional offline teaching, the deep learning-oriented blended teaching model can effectively improve ICU nurses' ECMO transport knowledge mastery, learning immersion and online learning participation. It makes up for the competency deficiency of ICU nurses in ECMO transport and provides evidence for optimizing clinical nursing training strategies.
trial registrationChinese Clinical Trial Registry, Registration Number: ChiCTR2500115168, Public Access Link https://www.chictr.org.cn/showproj.html?proj=292586 . The registration date is December 23, 2025.
Indexed as
Identifiers
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.