The Worker Rehabilitation Questionnaire (WRQ) is a web-enabled, evidence-based tool for producing job information to support decision-making about employment or re-employment of individuals with functional limitations or decision-making about work disability. It offers significant advantages over other approaches commonly used for similar purposes.

Early in evaluation and treatment of psychological distress or psychological disorder that involves work disability, clinicians should specify reactivation and return-to-work plans and links to employers. The WRQ can be helpful in this regard by providing a structure for doing so. Further, to the extent that the WRQ facilitates assessment of employability and control of work disability, its application should prove highly cost-effective.

The WRQ relies on a subset of 150 job elements from the Position Analysis Questionnaire (PAQ), a standardized, widely used method of job analysis, and the empirical PAQ database that contains results from PAQ analysis of hundreds of thousands of jobs.

A key component of the WRQ is a set of algorithms that are used to query the database. One advantage to using the WRQ is that it provides a standardized vocabulary and syntax for discussion of functional limitation and employability or work disability between health service providers, patients, employers, insurers, attorneys, judges and others.

Another advantage is that clinicians can use the WRQ to structure job-related functional capacity evaluations (FCEs) and identify need for reasonable job accommodation after disabling injury or illness.

A further WRQ advantage is its objectivity. A user can select job elements from the PAQ/WRQ to model individuals' functional limitations and run an algorithm against a PAQ database to generate lists of jobs that take functional limitations into account. Another algorithm compares two separate job-matching reports that are generated under different assumptions or parameters and reports the jobs listed in common on the two reports to assess consistency of results. A third algorithm calculates intraclass correlation to enable assessment of inter-rater or intrarater reliability. This algorithm also shows item-by-item differences between two sets of ratings, information necessary for improving rater agreement.