Mental Illness in the 2 Years Prior to Pregnancy in a Population With Traumatic Brain Injury: A Cross-Sectional Study: La maladie mentale dans les deux ans précédant une grossesse dans une population souffrant de lésion cérébrale traumatique : une étude transversale
Bibliographic record
Abstract
OBJECTIVE: Existing studies, in mostly male samples such as veterans and athletes, show a strong association between traumatic brain injury (TBI) and mental illness. Yet, while an understanding of mental health before pregnancy is critical for informing preconception and perinatal supports, there are no data on the prevalence of active mental illness before pregnancy in females with TBI. We examined the prevalence of active mental illness ≤2 years before pregnancy (1) in a population with TBI, and (2) in subgroups defined by sociodemographic, health, and injury-related characteristics, all compared to those without TBI. METHOD: This population-based cross-sectional study was completed in Ontario, Canada, from 2012 to 2020. Modified Poisson regression generated adjusted prevalence ratios (aPRs) of active mental illness ≤2 years before pregnancy in 15,585 females with TBI versus 846,686 without TBI. We then used latent class analysis to identify subgroups with TBI according to sociodemographic, health, and injury-related characteristics and subsequently compared them to females without TBI on their outcome prevalence. RESULTS: Females with TBI had a higher prevalence of active mental illness ≤2 years before pregnancy than those without TBI (44.1% vs. 25.9%; aPR 1.46, 95% confidence interval, 1.43 to 1.49). There were 3 TBI subgroups, with Class 1 (low-income, past assault, recent TBI described as intentional and due to being struck by/against) having the highest outcome prevalence. CONCLUSIONS: Females with TBI, and especially those with a recent intentional TBI, have a high prevalence of mental illness before pregnancy. They may benefit from mental health screening and support in the post-injury, preconception, and perinatal periods. PLAIN LANGUAGE TITLE: Mental illness in the 2 years before pregnancy in a population with traumatic brain injury.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".