Psychometric evaluation of a new measure of fear avoidance behavior after mild traumatic brain injury using Rasch Analysis.
Bibliographic record
Abstract
IntroductionFear avoidance is a known risk factor for chronic disability after musculoskeletal injury and has been shown to be associated with a range of adverse outcomes after mild traumatic brain injury (MTBI). A psychometrically valid measure of fear avoidance behavior after MTBI could help advance the field. In this study, we examined the psychometric properties of a 16-item fear avoidance behavior questionnaire in a sample of people after MTBI. The Fear Avoidance Behavior in Traumatic Brain Injury questionnaire (FAB-TBI) is comprised of items from existing fear avoidance behavior measures (primarily in chronic pain literature), selected on the basis of principal component and factor analyses in prior studies, and presented with uniform Likert-type response options. Ordinal scales may not discriminate precisely between fear avoidance behaviors across individuals. The current study aimed to improve precision and item functioning of the FAB-TBI by applying Rasch analysis.Materials and MethodsAdults (n=120) who were recruited from outpatient clinics in Vancouver and Calgary following MTBI completed the FAB-TBI at a median of six weeks post-injury (IQR 4-8). First, using Classical Test Theory methods we examined item-total correlations, Cronbachu2019s u03b1 and dimensionality. We used Principal components and Varimax rotation to explore dimensionality. Second, we used Rasch analysis to determine overall and individual item fit to the Rasch model, differential item functioning (DIF), local independence, unidimensionality and person-separation reliability of the FAB-TBI using RUMM2030 software.ResultsParticipants were on average 39 years old (SD 12.5), predominantly Caucasian (68%), with slightly more women (61%) than men. The most common injury mechanism was motor vehicle accident (39%). FAB-TBI item-total correlations were all above 0.3, Cronbachu2019s u03b1=0.9, and we observed a clear four-factor structure (activity avoidance, general pain concern, headache avoidance, general symptom avoidance). Initial analysis indicated the FAB-TBI demonstrated adequate but not perfect fit with the Rasch model with one misfitting item. Best fit to the unidimensional Rasch model was achieved after locally dependent items were combined into four subtests based on the initial exploratory factor analysis, retaining the misfitting item (Chi square=3.4, df 8, p=0.9). Finally the modified FAB-TBI demonstrated high internal consistency (Person Separation Index=0.8); there was no DIF across person factors examined. There was DIF by recruitment site, but this finding was unstable, requiring further evaluation. The results support retaining the original response format and content of the measure. From these analyses we created ordinal-to-interval conversion tables that will allow more precise assessment of fear avoidance behavior across individuals on an interval scale.Conclusions:With minor modifications, the FAB-TBI demonstrated adequate properties as a unidimensional scale. Rasch analyses supported the FAB-TBI as a psychometrically sound measure of fear avoidance behavior in adults after MTBI, with potential to assist clinicians develop targeted MTBI interventions.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 0.000 |
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".