JMIR Publications

ABSTRACT

Background:

The rapid development of digital and intelligent medical technologies is profoundly reshaping the clinical work patterns of physicians, introducing new job demands and job resources into their clinical practice. However, existing measurement instruments have not captured these specific changes and novel challenges posed by such technologies, resulting in a lack of corresponding assessment tools, which limits in-depth quantitative research in this field.

Objective:

This study aims to develop and validate the Job Demands Scale (JDS) and Job Resources Scale (JRS) for physicians suitable for digital and intelligent healthcare scenarios.

Methods:

Building upon the foundation of prior qualitative interviews and literature review, the dimensions of the scales and a corresponding pool of measurement items were constructed. The scales underwent content revision through two rounds of Delphi expert consultation (N=18) and cognitive interviews (N=6). Subsequently, an online questionnaire survey was conducted with 1,016 clinicians using convenience sampling. The psychometric properties of the scales were evaluated through item analysis, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and reliability testing.

Results:

The finalized JDS comprises 22 items across six dimensions: Human-Machine Interaction Burden, Technology Output Risk, Information Security Burden, Occupational Substitution Risk, Doctor-Patient Communication Burden, and Technology Dependence Risk. The JRS consists of 23 items, also organized into six dimensions: Decision-Making Support, Risk Prevention Support, Workload Reduction Tools, Doctor-Patient Collaborative Platform, Precision Efficiency Support, and Clinical Competence Support. EFA indicated that the six factors of the JDS cumulatively explained 71.10% of the variance, and the six factors of the JRS cumulatively explained 58.98% of the variance. CFA demonstrated good model fit for both scales. For the JDS, the composite reliability (CR) values for the dimensions ranged from 0.758 to 0.869, and the average variance extracted (AVE) values ranged from 0.441 to 0.687. For the JRS, the CR values ranged from 0.640 to 0.792; however, the AVE values were relatively low, ranging from 0.339 to 0.490. The overall Cronbach's α coefficients for the JDS and JRS were 0.944 and 0.923, respectively. These results demonstrate that both scales possess good preliminary reliability and validity. However, their dimensional structure and discriminant validity still require further optimization.

Conclusions:

The JDS and JRS developed in this study exhibit good psychometric properties and hold strong potential for effectively evaluating physicians' job demands and job resources within the context of digital-intelligent healthcare. This provides a scientific basis for subsequent related research and clinical management practices. It is noteworthy that the high correlations observed between the dimensions of both scales suggest that future analyses on the impact of job demands and job resources on physicians' work in digital-intelligent healthcare settings should pay attention to the synergistic effects of these elements. Furthermore, the content of the scales should be continuously updated alongside the advancement of digital-intelligent medical technologies.