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
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.