The Emergency Response Capabilities of Nurses (Including Occupational Protection, Critical Thinking, Communication Skills and Humanistic Care, Etc.)
Conditions
Brief summary
The goal of this randomized controlled trial is to evaluate the immediate efficacy of a Large Language Model (LLM)-assisted training program in enhancing nurses' emergency response capabilities in 204 practicing nurses with ≤5 years of experience from tertiary hospitals in Guiyang, China, focusing on public health emergencies (PHEs). The main questions it aims to answer are: 1. Does LLM-assisted training improve nurses' comprehensive emergency response capabilities in PHEs? 2. Does it specifically enhance rescue skills and occupational protection abilities? Researchers will compare the experimental group (receiving routine PHE training + LLM-assisted learning) to the control group (receiving routine PHE training only) to see if LLM supplementation leads to significantly greater improvements in targeted emergency competencies. Participants will: Complete pre- and post-training assessments (Nurse Self-Assessment Scale for Emergency Response Ability, Nurse's Emergency Response Capacity Scale for PHEs). Undergo a one-month PHE training program. (Experimental Group Only): Use LLMs for knowledge review, question answering, and exploring unfamiliar concepts during the training period.
Interventions
OTHERLLM-Assisted Public Health Emergency Training Program
A hybrid training program integrating the hospital's standard public health emergency (PHE) curriculum with Large Language Model (LLM) technology as an auxiliary learning tool. Participants receive: * Standardized PHE training (online lectures + offline simulations) covering professional knowledge, skills, and emergency drills (e.g., infectious disease response, trauma management). * LLM-enabled interactive support: Structured guidance to use LLMs for: Reviewing session content Resolving knowledge uncertainties via Exploring unfamiliar PHE concepts • Duration: 1 month, with 20-minute sessions. Distinguishing feature: Uses LLMs to dynamically adapt to individual learning needs, enabling on-demand knowledge reinforcement and overcoming spatiotemporal limitations of traditional training.
OTHERStandard Public Health Emergency Training Program
The hospital's existing public health emergency (PHE) training program without AI augmentation. Participants receive: * Identical core content as the experimental group: Professional knowledge, skills training, and emergency drills for PHE response (e.g., disaster protocols, infection control). * Explicit restriction: Prohibited from using LLMs or any AI tools for learning support. * Delivery: Hybrid format (online + offline), 1-month duration, 20-minute sessions. Distinguishing feature: Represents traditional training methods reliant on instructor-led content without personalized, on-demand AI-driven reinforcement.
Sponsors
The Affiliated Hospital Of Guizhou Medical University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SUPPORTIVE_CARE
Masking
DOUBLE (Subject, Investigator)
Eligibility
Sex/Gender
ALL
Age
18 Months to 35 Months
Healthy volunteers
Yes
Inclusion criteria
* Holds a valid nursing professional qualification certificate； * ≤5 years of nursing work experience； * Voluntarily agrees to participate in the training program。
Exclusion criteria
* Inability to complete the 1-month training program (e.g., planned leave, transfer, or resignation during the study period) * Prior experience using Large Language Models (LLMs) for professional training (to avoid confounding effects) * Refusal to comply with group assignment protocols (e.g., control group participants attempting to use LLMs)
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Comprehensive Emergency Response Capability Total Score Nurse's Self-Assessment Capability Total Score | Baseline (pre-training) and immediately post-intervention (after 1 month of training) |
Countries
China
Outcome results
None listed