Psychiatric Disorder Clinical Trial
Official title:
Investigating Psychosocial Predictors & Employment Outcomes of Enhanced IPS Intervention A Prospective Cohort Study
The purpose of this cohort study, using an existing database, is to seek to describe the
relationship between psychosocial factors (independent variables) and employment outcomes
(dependent variables) in a population of people with complex mental health problems receiving
evidence-based supported employment (EBSE) augmented with theory-driven occupational therapy
interventions. The intention is to understand the predictive nature of psychosocial factors
on the positive outcome of success in achieving paid employment.
The Worker role interview (WRI) is routinely used in the research site; an National health
Service (NHS) occupational therapy led vocational rehabilitation delivering EBSE service for
people with complex mental health problems. An existing database of initial assessments and
employment outcomes is available for investigation.
The cohort study will be undertaken utilising a secondary dataset from an established
vocational rehabilitation service (VRS). This service is experienced in providing
intervention based on individual placement and support (IPS) with established high fidelity
to the model. The team's interventions are also guided by the model of human occupation,
(MOHO) and the Worker Role Interview is routinely completed with all patients accessing the
service. The Worker Role Interview is a valid reliable measure of psychosocial items
measuring of readiness for employment.
The consistency of intervention will be ensured as the service is routinely appraised for
fidelity to the IPS by two experts, using a standardised assessment tool the IPS fidelity
scale and the team has received ongoing training and regular caseload supervision to
integrate evidence-based occupational therapy into practice. The experts appraising the
service are independent of both the clinical service and the proposed research.
The established VRS provides an assessment and intervention for people with complex mental
health problems. A dataset has been routinely collected over several years which is currently
used to evaluate the service.
To address potential limitations of secondary data analysis the researcher has an established
relationship with clinicians in the VRS and was involved in the selection of routine measures
now used to evaluate the service. It is, therefore, possible to be confident that a
consistent and standard dataset is available for analysis. Ongoing training and consultation
have been provided by the researcher to the clinical service to ensure the data quality.
Data Collection The data will be managed and transferred by a trained researcher on site at
both services.Data will be robustly anonymised prior to transfer. Data will be transferred
from NHS secure network to secure network within Queen Margaret University using a secure
email service approved by the Department of Health.
The dataset comprises; Research identification (ID) code Gender Age on Admission Ethnicity
Marital Status Living situation (alone/shared/supported accommodation) Quintile range of
Scottish Index of Multiple Deprivation Educational attainment Previous employment ICD10
Worker role interview items Date of assessment WRI Assesses Abilities & Limitations WRI
Expectation of Success in Work WRI Takes Responsibility WRI Commitment to Work WRI
Work-Related Goals WRI Enjoys Work WRI Pursues Interests WRI Appraises Work Expectations WRI
Influence of Other Roles WRI Work Habits WRI Daily Routines WRI Adapts Routine to Minimize
Difficulties WRI Perception of Physical Work Setting WRI Perception of Family and Peers WRI
Perception of Boss and/or Company WRI Perception of Co-workers Employment goal Date of final
goal review Outcome of goal (successful/ unsuccessful in attaining employment)
Sample; Based on the guideline of a minimum of 10 events per predictor variable, a sample
size of 200 would allow for the robust estimation of 8 variables in the model.
Analysis Plan The collected data will be subject to analysis with the use of multiple
logistic regression using the R data analysis software. Multiple logistic regression will
allow the construction of models which examine the effects of psychosocial predictors on the
probability of a person with complex mental health problems attaining paid employment.
Possible confounds which may influence outcomes will be controlled for (e.g. age, gender,
educational achievement, history of employment). Interpretation of the analysis will be
shared and discussed with clinicians who are expert in their field to ensure that findings
are considered from both a statistical significance and clinical significance position.
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