Evaluation of the Fear Avoidance Behavior after Traumatic Brain Injury Questionnaire
Bibliographic record
Abstract
Fear avoidance behavior is related to symptom persistence and disability in various health conditions, such as chronic pain. Fear avoidance behavior also may impact recovery from mild traumatic brain injury (mTBI), but no measure of this construct has been psychometrically validated for the mTBI population. Adults who sustained an mTBI (n = 159) were recruited from three outpatient mTBI clinics. Participants completed the new Fear Avoidance Behavior after Traumatic Brain Injury Questionnaire (FAB-TBI). The FAB-TBI includes 16 items drawn from well-established fear avoidance scales, primarily in the chronic pain literature. An exploratory factor analysis and Rasch analysis were conducted to evaluate the factor structure, dimensionality, and differential item functioning of the FAB-TBI. The FAB-TBI scale was found to have strong internal consistency (Cronbach's α = 0.9). Exploratory factor analysis suggested at least two distinct factors (activity avoidance and cogniphobia). Initial fit to the Rasch model was adequate, with one misfitting item. The model was not improved after removing the misfitting item. Best fit to the unidimensional Rasch model was achieved after items were combined into three super items based on exploratory factor analysis and retaining the misfitting item χ2(6, n = 159) = 2.1, p = 0.06). The FAB-TBI appears to be a psychometrically sound measure of fear avoidance behavior after mTBI. Conversion tables are made available to convert scores into interval-level data for future research.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".