Work-related and non-work-related mild traumatic brain injury: Associations with mental health and substance use challenges in a Canadian population-level survey
Bibliographic record
Abstract
BACKGROUND: Mild traumatic brain injury (mTBI) can profoundly impact overall health, employment, and family life. Incidence of mTBI in the workplace represents an important subgroup with poorer outcomes. Mental health (MH) and substance use (SU) challenges are a primary correlate of TBI, but are rarely assessed among individuals with a work-related (wr)-mTBI, particularly at a population-level. OBJECTIVE: This study aimed to assess the association between lifetime wr-mTBI and non-wr-mTBI and the experience of MH and SU challenges. METHODS: The 2019 Centre for Addiction and Mental Health (CAMH) Monitor is a cross-sectional telephone survey of adults aged≥18 years in Ontario, Canada, employing a stratified (six regions) two-stage (telephone number, respondent) list-assisted random digit dialing probability selection procedure (N = 1792). Adjusting for sociodemographic variables, binary logistic regression was conducted to assess the association between lifetime wr-mTBI and non-wr-mTBI (relative to no TBI) and four outcomes: hazardous use of alcohol and of cannabis, psychological distress, and fair/poor mental health. RESULTS: Adjusting for sociodemographic variables, non-wr-mTBI demonstrated increased odds of hazardous alcohol (AOR = 2.12, 95% CI = 1.41, 3.19) and cannabis use (AOR = 1.61, 95% CI = 1.05, 2.45), psychological distress (AOR = 1.68, 95% CI = 1.14, 2.49), and fair/poor mental health (AOR = 1.70, 95% CI = 1.11, 2.59). Lifetime wr-mTBI demonstrated increased odds of reporting psychological distress (AOR = 3.40, 95% CI = 1.93, 5.97) and fair/poor mental health (AOR = 2.16, 95% CI = 1.12, 4.19) only. CONCLUSIONS: Non-wr-mTBI was associated with both MH and SU, whereas wr-mTBI was associated with MH only. MH outcomes were more strongly associated with wr-mTBI than non-wr-mTBI. Physicians, employers, and insurers need to consider the potential association between wr-mTBI and MH, and provide care accordingly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".