Fear avoidance and return to work after mild traumatic brain injury
Bibliographic record
Abstract
OBJECTIVES: Fear avoidance is associated with symptom persistence after mild traumatic brain injury (mTBI). In this study, we investigated whether fear avoidance was associated with other outcomes such as return to work-related activity (RTW). MATERIALS AND METHODS: We analyzed associations between fear avoidance and RTW 6-9 months after mTBI, in two merged prospective mTBI cohorts. Adult participants aged 16 or over (n=175), presenting to outpatient services in New Zealand within 3 months of their injury, who were engaged in work-related activity at the time of injury, were included. Participants completed the Fear Avoidance Behavior after Traumatic Brain Injury (FAB-TBI) questionnaire at enrollment and 6 months later. Associations between FAB-TBI scores and RTW outcome were analyzed using multivariate approaches. RESULTS: Overall, 53% of participants had RTW by 6-9 months after mTBI. While early fear avoidance was weakly associated with RTW, persistent high fear avoidance between study assessments or increasing avoidance with time were associated with greater odds of still being off work 6-9 months after injury. CONCLUSIONS: Pervasive and increasing avoidance of symptom triggers after mTBI were associated with lower rates of RTW 6-9 months after mTBI. Further research is needed to better understand transition points along the recovery trajectory where fear avoidance behaviors fade or increase 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".