Utilizing diverse cross-sectional assessment templates to instruct novice nurses in the neurology department about typical diseases

The utilization of diverse cross-sectional assessment templates and instructional videos for typical diseases in the training of novice nurses in the neurology department enhanced nursing quality, improved clinical practical abilities, and improved patient safety.

Authors

Institutions

Publication Details

Journal
Annals of Medicine
Published
2024-12-10
DOI
https://doi.org/10.1080/07853890.2024.2440126
Primary Topic
Emergency and Acute Care Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Utilizing diverse cross-sectional assessment templates to instruct novice nurses in the neurology department about typical diseases

Qingmei Wang, Dongping Shi, Shi-Jing Zhang
Annals of Medicine
Emergency and Acute Care Studies
article

Utilizing diverse cross-sectional assessment templates to instruct novice nurses in the neurology department about typical diseases

Qingmei Wang, Dongping Shi, Shi-Jing Zhang
article en

Abstract

The utilization of diverse cross-sectional assessment templates and instructional videos for typical diseases in the training of novice nurses in the neurology department enhanced nursing quality, improved clinical practical abilities, and improved patient safety.

Annals of MedicineVol. 57(1)
Huaian First People’s Hospital (CN), Nanjing Medical University (CN)
Quality Education
Openalex Percentile: Top 60%
Emergency and Acute Care Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.