JMIR Publications

ABSTRACT

Background:

There is an urgent need worldwide for qualified health professionals. High attrition rates among health professionals, combined with a predicted rise in life expectancy, further emphasize the need for additional health professionals. Work-related stress is a major concern in health professionals, affecting both the well-being of health professionals and the quality of patient care.

Objective:

This scoping review aims to identify processes and methods for the automatic detection of work-related stress among health professionals using Natural Language Processing (NLP) and text mining techniques.

Methods:

The review follows JBI methodology and PRISMA guidelines for scoping reviews. Studies published in English, German, or French from 2013 to present will be considered. The databases to be searched include Medline (via PubMed), CINAHL, PubMed, Cochrane, ACM Digital Library, and IEEE Xplore. Sources of unpublished studies and grey literature to be searched will include ProQuest Dissertations and Theses and OpenGrey. Two reviewers will independently retrieve full-text studies and extract data. The collected data will be organized in tables, graphs, and a narrative summary.

Results:

The review will use tables and graphs to present data on studies' distribution by year, country, activity field, and research methods. Results synthesis involves identifying, grouping, and categorizing, followed by a narrative summary and discussion of implications for practice and research. We anticipate that the final outcomes will be presented in a systematic scoping review by June 2024.

Conclusions:

This review fills a literature gap by identifying automated work-related stress detection among health professionals using NLP and text mining, providing insights, an innovative approach, and identifying research needs for further systematic reviews.