Journal of Occupational Rehabilitation

, Volume 15, Issue 4, pp 459-474

First online:

Prognosis and the Identification of Workers Risking Disability: Research Issues and Directions for Future Research

  • Steven J. LintonAffiliated withDepartment of Behavioral, Social and Legal Sciences—Psychology, Örebro University Email author 
  • , Doug GrossAffiliated withDepartment of Physical Therapy, University of Alberta
  • , Izabela Z. SchultzAffiliated withDepartment of Educational and Counselling Psychology and Special Education, University of British Columbia
  • , Chris MainAffiliated withCalderbank Research Unit, University of Manchester
  • , Pierre CôtéAffiliated withInstitute for Work and Health
  • , Glenn PranskyAffiliated withCenter for Disability Research, Liberty Mutual Research Institute for Safety
  • , William JohnsonAffiliated withSchool of Health Management and Policy, Arizona State University

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Introduction: Screening procedures based on prognostic data are an important prerequisite for prevention of disability due to low-back pain. This paper reviews the research on prognosis to delineate the most pertinent research challenges, and outlines directions for future research to improve the scientific quality and screening accuracy of prognostic efforts. Methods: Reviews of prognosis research were examined to identify key methodological and research issues. Results: Certain issues such as sampling procedures, research designs, data analyses, prognostic indicators, and follow-up procedures limit the value of prior studies. Absence of a clear conceptual framework hampers interpretation of findings and moving research questions forward. The recurrent nature of back pain and the need to effectively include the impact of employer actions and the job market were also identified as significant issues. Conclusions: Future research will be enhanced by addressing conceptual and definitional issues, applying tested and sensible measures, and careful follow-up of the study population.

Key Words

screening disability conceptual models methodology