Towards an ICT-based psychology: E-psychology

Natural Language Processing for Work-Related Stress Detection Among Health Professionals: Protocol for a Scoping Review

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 among 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

This review follows Joanna Briggs Institute Methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The inclusion criteria for this scoping review encompass studies involving health professionals using NLP for work-related stress detection while excluding studies involving other professions or children. The review focuses on various aspects, including NLP applications for stress detection, criteria for stress identification, technical aspects of NLP, and implications of stress detection through NLP. Studies within health care settings using diverse NLP techniques are considered, including experimental and observational designs, aiming to provide a comprehensive understanding of NLP's role in detecting stress among health professionals. 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 Sources of unpublished studies and gray literature to be searched will include ProQuest Dissertations & Theses and OpenGrey.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 qualitative narrative summary. This 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. The final scoping review will include a narrative written report detailing the search and study selection process, a visual representation using a PRISMA-ScR flow diagram, and a discussion of implications for practice and research.

RESULTS

We anticipate the 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 on an innovative approach, and identifying research needs for further systematic reviews. Despite promising outcomes, acknowledging limitations in the reviewed studies, including methodological constraints, sample biases, and potential oversight, is crucial to refining methodologies and advancing automatic stress detection among health professionals.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/56267.

Social robots as treatment agents: Pilot randomized controlled trial to deliver a behavior change intervention

Social robots are increasingly demonstrating effectiveness as low-intensity behavior change agents. Key targets for these behavioral interventions include daily lifestyle behaviors with significant health consequences, such as the consumption of high-calorie foods and drinks ('snacks'). A pilot randomized controlled trial using a stepped-wedge design was conducted to determine the efficacy of a motivational intervention by an autonomous robot, to help reduce high-calorie snacks. Twenty-six adults were randomized to receive Immediate or 4-week Delayed treatment, with assessments at Baseline and Weeks 4 and 8. The treatment comprised motivation enhancement and self-management training using mental imagery (Functional Imagery Training). A significant condition by time effect for snack episode reduction was obtained, F(2, 32.06) = 4.30, p = .022. The Immediate condition significantly reduced snacking between Baseline and Week 4 (d = -1.06), while the Delayed condition did not (d = -0.08). Immediate participants maintained their improvement between Weeks 4 and 8 (d = -0.18), and Delayed participants then showed a significant fall (d = -1.42). Overall, 'Immediate' participants decreased their snack episodes by 54% and 'Delayed' decreased by 62% from Baseline to Week 8, and an average weight reduction of 4.4 kg was seen across over the first 2 weeks of treatment. Four weeks after starting the intervention, both conditions had significant increases in perceived confidence to control snack intake for time duration, specific scenarios and emotional states (d = 0.61 to 1.42). Working alliance was significantly correlated with reduced snack episodes. The pilot's results appear to suggest that the robot-delivered intervention may be as effective as a human clinician delivering a similar intervention. The robot-delivered pilot achieved similar snack episode reduction in the first four weeks (FIT-R, 55%) when compared with the human-delivered version by a trained clinician (FIT-H, 49%). Overall, the results provide preliminary evidence for an autonomous social robot to deliver a low-intensity treatment on dietary intake without the need for human intervention. Future trials should extend the deployment of the robot-delivered intervention protocol to other low-intensity behavioral outcomes.

Assessing the impact of organizational culture on achieving business excellence with a moderating role of ICT

Purpose

The purpose of this paper is to discover the impact of different dimensions of organizational culture (mission culture, adaptability culture, involvement culture and consistency culture) on business excellence results criteria (customer results, people results, society results and business results) in the United Arab Emirates (UAE) and explore the moderating role of information and communication technology (ICT) use in both service and manufacturing industries.

Design/methodology/approach

Data were collected by questionnaire from 448 managers in nine companies that have won the Sheikh Khalifa Excellence Award in the last three years. Structural equation modeling was used to examine the data.

Findings

Organizational culture is significantly related to business excellence. However, these effects varied for different business excellence criteria. Three organizational culture types had a significant positive role in achieving excellent customer-related results. All four types of organizational culture had a positive role in achieving excellent people-related results. Only two culture types had significant role in achieving excellent society-related results. Business results were positively related to a balance between the four types of organizational culture. ICT use moderated the relationship between organizational culture and results related to customers, people and business, but not society.

Research limitations/implications

This study had some conceptual limitations. In particular, it considered the organizational culture as four types in the research model, but without structuring the indices under each type. It also had some methodological limitations. It was cross-sectional and used a self-administered questionnaire, which means that no causal relationships can be implied, and there may have been some bias in responding.

Originality/value

This is one of the first studies that investigate the relationship between organizational culture and business excellence in UAE excellence award-winning companies.

Automated Assessment of Patients' Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining

Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms-including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model-were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners' diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients' self-expression behavior, thus helping clinicians identify potential patients from an early stage.