Ignite Creation Date:
2025-12-25 @ 2:44 AM
Ignite Modification Date:
2025-12-26 @ 1:24 AM
Study NCT ID:
NCT07141433
Status:
COMPLETED
Last Update Posted:
2025-08-26
First Post:
2025-08-19
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Development and Evaluation of a Large Language Model - Based Training Program for Nurses in Public Health Emergencies
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'DOUBLE', 'whoMasked': ['PARTICIPANT', 'INVESTIGATOR']}, 'primaryPurpose': 'SUPPORTIVE_CARE', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 204}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2024-10-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-08', 'completionDateStruct': {'date': '2024-12-31', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-08-19', 'studyFirstSubmitDate': '2025-08-19', 'studyFirstSubmitQcDate': '2025-08-19', 'lastUpdatePostDateStruct': {'date': '2025-08-26', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-08-26', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2024-12-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': "Comprehensive Emergency Response Capability Total Score Nurse's Self-Assessment Capability Total Score", 'timeFrame': 'Baseline (pre-training) and immediately post-intervention (after 1 month of training)'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['The Emergency Response Capabilities of Nurses (Including Occupational Protection, Critical Thinking, Communication Skills and Humanistic Care, Etc.)']}, 'descriptionModule': {'briefSummary': "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:\n\n1. Does LLM-assisted training improve nurses' comprehensive emergency response capabilities in PHEs?\n2. 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.\n\nParticipants will:\n\nComplete pre- and post-training assessments (Nurse Self-Assessment Scale for Emergency Response Ability, Nurse's Emergency Response Capacity Scale for PHEs).\n\nUndergo a one-month PHE training program. (Experimental Group Only): Use LLMs for knowledge review, question answering, and exploring unfamiliar concepts during the training period."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD'], 'maximumAge': '35 Months', 'minimumAge': '18 Months', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Holds a valid nursing professional qualification certificate；\n* ≤5 years of nursing work experience；\n* Voluntarily agrees to participate in the training program。\n\nExclusion Criteria:\n\n* Inability to complete the 1-month training program (e.g., planned leave, transfer, or resignation during the study period)\n* Prior experience using Large Language Models (LLMs) for professional training (to avoid confounding effects)\n* Refusal to comply with group assignment protocols (e.g., control group participants attempting to use LLMs)'}, 'identificationModule': {'nctId': 'NCT07141433', 'briefTitle': 'Development and Evaluation of a Large Language Model - Based Training Program for Nurses in Public Health Emergencies', 'organization': {'class': 'OTHER', 'fullName': 'The Affiliated Hospital Of Guizhou Medical University'}, 'officialTitle': 'Development and Evaluation of a Large Language Model - Based Training Program for Nurses in Public Health Emergencies', 'orgStudyIdInfo': {'id': '[2022]-4-2-2'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'OTHER', 'label': 'LLM-Assisted PHE Training Group', 'description': 'Participants in this arm receive the routine hospital-based public health emergency (PHE) training program supplemented with Large Language Model (LLM) technology for auxiliary learning. During the 1-month training period, they are instructed and encouraged to use LLMs for:\n\n* Reviewing knowledge covered in training sessions\n* Answering questions and clarifying uncertainties\n* Exploring unfamiliar concepts related to PHE response (Intervention: Standard PHE curriculum + LLM-enabled interactive learning support)', 'interventionNames': ['Other: LLM-Assisted Public Health Emergency Training Program', 'Other: Standard Public Health Emergency Training Program']}, {'type': 'OTHER', 'label': 'Standard PHE Training Group', 'description': 'Participants in this arm receive only the routine hospital-based public health emergency (PHE) training program. They are explicitly restricted from using LLMs or any other artificial intelligence tools for assisted learning throughout the 1-month training period.\n\n(Intervention: Standard PHE curriculum without AI augmentation)', 'interventionNames': ['Other: Standard Public Health Emergency Training Program']}], 'interventions': [{'name': 'LLM-Assisted Public Health Emergency Training Program', 'type': 'OTHER', 'description': "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:\n\n* Standardized PHE training (online lectures + offline simulations) covering professional knowledge, skills, and emergency drills (e.g., infectious disease response, trauma management).\n* LLM-enabled interactive support: Structured guidance to use LLMs for:\n\nReviewing session content Resolving knowledge uncertainties via Exploring unfamiliar PHE concepts\n\n• 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.", 'armGroupLabels': ['LLM-Assisted PHE Training Group']}, {'name': 'Standard Public Health Emergency Training Program', 'type': 'OTHER', 'description': "The hospital's existing public health emergency (PHE) training program without AI augmentation. Participants receive:\n\n* Identical core content as the experimental group: Professional knowledge, skills training, and emergency drills for PHE response (e.g., disaster protocols, infection control).\n* Explicit restriction: Prohibited from using LLMs or any AI tools for learning support.\n* Delivery: Hybrid format (online + offline), 1-month duration, 20-minute sessions.\n\nDistinguishing feature: Represents traditional training methods reliant on instructor-led content without personalized, on-demand AI-driven reinforcement.", 'armGroupLabels': ['LLM-Assisted PHE Training Group', 'Standard PHE Training Group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '550004', 'city': 'Guiyang', 'state': 'Guizhou', 'country': 'China', 'facility': 'The Affiliated Hospital of Guizhou Medical University', 'geoPoint': {'lat': 26.58333, 'lon': 106.71667}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The Affiliated Hospital Of Guizhou Medical University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}