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Accepted for/Published in: JMIR Research Protocols

Date Submitted: Jan 30, 2026
Date Accepted: May 29, 2026

The final, peer-reviewed published version of this preprint can be found here:

Design, Development, and Validation of a Chatbot to Support Health Care Professionals Experiencing Workplace Aggression: Protocol for a Mixed Methods Study

García-Viola A, Márquez-Hernández VV, Garrido-Molina JM, Alcayde-García A, Rodríguez-García MC

Design, Development, and Validation of a Chatbot to Support Health Care Professionals Experiencing Workplace Aggression: Protocol for a Mixed Methods Study

JMIR Res Protoc 2026;15:e92511

DOI: 10.2196/92511

PMID: 42314161

Design, Development and Validation of a Chatbot (SANIDAD SEGURA) to Support Healthcare Professionals Experiencing Workplace Aggression: protocol for a mixed-methods study

  • Alba García-Viola; 
  • Verónica V. Márquez-Hernández; 
  • José Miguel Garrido-Molina; 
  • Alfredo Alcayde-García; 
  • Mª Carmen Rodríguez-García

ABSTRACT

Background:

Workplace violence against healthcare professionals has increased worldwide, leading to negative psychological, professional, and organizational outcomes. Despite existing prevention and reporting programs, underreporting and lack of accessible, confidential support persist. Digital health tools, including chatbots, may offer scalable support, guidance, and follow-up for affected professionals.

Objective:

This study aims to design, develop, and validate a chatbot (AGRESTOP) to assist healthcare professionals who experience workplace aggression, and to evaluate its usability, readability, and preliminary effectiveness in a pilot study.

Methods:

A mixed-methods study will be conducted in three phases: (1) design and development of the chatbot based on literature review, legal and institutional protocols, and expert consensus; (2) content validation and usability testing using standardized instruments; and (3) pilot deployment for feasibility and acceptability assessment among healthcare professionals working in emergency and critical care settings in Almería (Spain). Quantitative data will be collected using the System Usability Scale (SUS) and INFLESZ readability scale, while qualitative feedback will be obtained through user comments and interviews. Statistical analyses will include descriptive and inferential methods.

Results:

Data generated through the chatbot will contribute to building a registry and database to describe incidence, prevalence, and risk factors associated with workplace aggression, allowing for the identification of vulnerable groups and high-risk contexts. These insights will inform the design of evidence-based screening, prevention, and intervention strategies, reinforcing organizational policies that promote safer, more supportive healthcare environments.

Conclusions:

This study addresses the increasing incidence of workplace violence against healthcare professionals through the development of a new chatbot (AGRESTOP). This intervention seeks to facilitate the identification, support, and follow-up of affected individuals, while minimizing the adverse effects of such events on their physical and psychological well-being, social interaction, and professional performance. AGRESTOP will enable confidential case reporting and provide access to tailored medical, psychological, and legal resources, as well as information about institutional support services. This project represents a crucial step toward implementing an integrated digital framework for the detection, management, and prevention of workplace violence in healthcare settings.


 Citation

Please cite as:

García-Viola A, Márquez-Hernández VV, Garrido-Molina JM, Alcayde-García A, Rodríguez-García MC

Design, Development, and Validation of a Chatbot to Support Health Care Professionals Experiencing Workplace Aggression: Protocol for a Mixed Methods Study

JMIR Res Protoc 2026;15:e92511

DOI: 10.2196/92511

PMID: 42314161

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