Identifying ICF Categories for a Generic Core Set
Author Information
Author(s): Cieza Alarcos, Geyh Szilvia, Chatterji Somnath, Kostanjsek Nenad, Üstün Bedirhan T, Stucki Gerold
Primary Institution: Ludwig-Maximilian University, Munich, Germany
Hypothesis
Can candidate ICF categories be identified for a Generic ICF Core Set using regression modeling?
Conclusion
The study proposes a selection strategy for ICF categories, leading to a list of suitable candidates for further consideration in developing a Generic ICF Core Set.
Supporting Evidence
- Fourteen different regression models were performed.
- The variance explained by the models ranged from 22.27% to 24.0%.
- The ICF category that explained the highest variance was sensation of pain.
- Thirteen candidate ICF categories were proposed for further consideration.
Takeaway
Researchers looked at how different health issues affect people's daily lives and found some important categories to help describe these issues better.
Methodology
A multi-centre, cross-sectional study using regression modeling to analyze data from patients with chronic health conditions.
Potential Biases
Potential bias due to convenience sampling and exclusion of certain patient demographics.
Limitations
The study's findings may not be generalizable due to the specific patient population and the low variance explained by the ICF categories.
Participant Demographics
58.6% female, ages 18-84, with various chronic health conditions.
Statistical Information
P-Value
<0.01
Statistical Significance
p<0.01
Digital Object Identifier (DOI)
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