Using a Mobile App-Based International Classification of Functioning, Disability, and Health Set to Assess the Functioning of Spinal Cord Injury Patients: Rasch Analysis
Bibliographic record
Abstract
BACKGROUND: The International Classification of Functioning, Disability, and Health (ICF) is a unified system of functioning terminology that has been used to develop electronic health records and assessment instruments. Its application has been limited, however, by its complex terminology, numerous categories, uncertain operationalization, and the training required to use it well. Together is a mobile health app designed to extend medical support to the families of spinal cord injury (SCI) patients in China. The app's core framework is a set of only 31 ICF categories. The app also provides rating guidelines and automatically transforms routine assessment results to the terms of the ICF qualifiers. OBJECTIVE: The goal of the research is to examine the suitability of the ICF set used in the app Together for use as an instrument for assessing the functioning of SCI patients. METHODS: A cross-sectional study was conducted including 112 SCI patients recruited before discharge from four rehabilitation centers in China between May 2018 and October 2019. Nurses used the app to assess patient functioning in face-to-face interviews. The resulting data were then subjected to Rasch analysis. RESULTS: After deleting two categories (family relationships and socializing) and one personal factor (knowledge about spinal cord injury) that did not fit the Rasch model, the body functions and body structures, activities and participation, and contextual factors components of the ICF exhibited adequate fit to the Rasch model. All three demonstrated acceptable person separation indices. The 28 categories retained in the set were free of differential item functioning by gender, age, education level, or etiology. CONCLUSIONS: Together overcomes some of the obstacles to practical application of the ICF. The app is a reliable assessment tool for assessing functioning after spinal cord injury.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".