Mild traumatic brain injury caused by workplace violence in a US workers’ compensation system
Bibliographic record
Abstract
OBJECTIVES: Approximately 81% of traumatic brain injury cases are considered to be mild (mTBI), but few studies have reviewed mTBI caused by workplace violence (WPV). This study aimed to (1) determine the incidence of mTBI secondary to WPV in a statewide workers' compensation system using International Classification of Disease codes and (2) analyse and compare factors associated with return-to-work outcomes between WPV mTBI cases versus other mechanisms. METHODS: Using a retrospective cohort of claims data from the California Workers' Compensation Information System during 2015-2019, cases with a return-to-work date were classified as WPV if the injury description contained keywords such as assault, gunpoint, harassed, intimidated, punch, threat, robbery, violent or verbal abuse. RESULTS: Of the 14 089 mTBI claims analysed in this study, 11.2% were caused by WPV. When comparing WPV to non-WPV claims, the variables with statistically significant (p≤0.001) differences were age, income, industry and job class. There were no significant differences between groups for leave duration. In a linear mixed model, the variable of interest (WPV) was not associated with recovery duration after adjusting for other factors. CONCLUSION: To our knowledge, this is the first study to examine WPV mTBI claims in the USA. The findings suggest that the public administration, education and healthcare and social services industries are at higher risk for WPV mTBI. WPV and job class were the only modifiable factors in the model and therefore should be the focus of additional 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".