Association between traumatic brain injury and mental health care utilization: evidence from the Canadian Community Health Survey
Bibliographic record
Abstract
BACKGROUND: Mental health disorders are a common sequelae of traumatic brain injury (TBI) and are associated with worse health outcomes including increased mental health care utilization. The objective of this study was to determine the association between TBI and use of mental health services in a population-based sample. METHODS: Using data from a national Canadian survey, this study evaluated the association between TBI and mental health care utilization, while adjusting for confounding variables. A log-Poisson regression model was used to estimate unadjusted and adjusted prevalence ratios (PR) and 95% confidence intervals (CI). RESULTS: The study sample included 158,287 TBI patients and 25,339,913 non-injured individuals. Compared with those were not injured, TBI patients reported higher proportions of chronic mental health conditions (27% vs. 12%, p < 0.001) and heavy drinking (33% vs. 24%, p = 0.005). The adjusted prevalence of mental health care utilization was 60% higher in patients with TBI than those who were not injured (PR = 1.60, 95%; CI 1.05-2.43). CONCLUSIONS: This study suggests that chronic mental health conditions and heavy drinking are more common in individuals with TBI. The prevalence of mental health care utilization is 60% higher in TBI patients compared with those who are not injured after adjusting for sociodemographic factors, mental health conditions, and heavy drinking. Future longitudinal research is required to examine the temporality and direction of the association between TBI and the use of mental health services.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".