Article

OBJECTIVE: To investigate the scaling properties of the Patient Categorisation
Tool (PCAT) as an instrument to measure complexity of rehabilitation needs.
DESIGN: Psychometric analysis in a multicentre cohort from the UK national
clinical database. PATIENTS:
A total of 8,222 patents admitted for specialist
inpatient rehabilitation following acquired brain injury.
METHODS: Dimensionality
was explored using principal components analysis with Varimax rotation, followed
by Rasch analysis on a random sample of n = 500.
RESULTS: Principal components
analysis identified 3 components explaining 50% of variance. The partial credit
Rasch model was applied for the 17-item PCAT scale using a "super-items"
methodology based on the principal components analysis results. Two out of 5
initially created super-items displayed signs of local dependency, which
significantly affected the estimates. They were combined into a single super-item
resulting in satisfactory model fit and unidimensionality. Differential item
functioning (DIF) of 2 super-items was addressed by splitting between age groups
(<65 and >/= 65 years) to produce the best model fit (chi2/df = 54.72, p = 0.235)
and reliability (Person Separation Index (PSI) = 0.79). Ordinal-to-interval
conversion tables were produced. CONCLUSION:
The PCAT has satisfied expectations
of the unidimensional Rasch model in the current sample after minor modifications, and demonstrated acceptable reliability for individual assessment of rehabilitation complexity.

Langue : ANGLAIS