Normative Data for the Fear Avoidance Behavior After Traumatic Brain Injury Questionnaire in a Clinical Sample of Adults With Mild TBI
Bibliographic record
Abstract
OBJECTIVE: Fear avoidance behavior after a concussion or mild traumatic brain injury (mTBI) is associated with a number of adverse outcomes, such as higher symptom burden, emotional distress, and disability. The Fear Avoidance Behavior after Traumatic Brain Injury Questionnaire (FAB-TBI) is a recently developed and validated self-report measure of fear avoidance after mTBI. The objective of this study was to derive clinical normative data for the FAB-TBI. To determine whether demographic stratification was necessary and to further support clinical interpretation, we also explored associations between fear avoidance behavior and demographic and injury variables. SETTING: Five concussion clinics in Canada. PARTICIPANTS: Adults who sustained an mTBI (N = 563). DESIGN: Cross-sectional. MAIN MEASURES: Participants completed the Fear Avoidance Behavior after Traumatic Brain Injury Questionnaire (FAB-TBI) and measures of postconcussion symptom burden (Rivermead Postconcussion Symptoms Questionnaire, Sport Concussion Assessment Tool-5) at clinic intake. RESULTS: Generalized linear modeling revealed that females reported more fear avoidance than males (95% CI = 0.66 to 2.75), indicating that FAB-TBI normative data should be stratified by sex. Differences between recruitment sites on FAB-TBI scores were reduced but not eliminated by controlling for potential confounds. Loss of consciousness (95% CI =0.61 to 2.76) and higher postconcussion symptom burden (95% CI = 0.79 to 1.03) were also associated with higher FAB-TBI scores, but time since injury was not (95% = CI -0.4 to 0.03). Tables to convert FAB-TBI raw scores to Rasch scores to percentiles are presented. CONCLUSION: These findings support clinical interpretation of the FAB-TBI and further study of fear avoidance after mTBI.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".