Traumatic Brain Injury History Among Individuals Using Mental Health and Addictions Services: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: Traumatic brain injury (TBI) has been increasingly linked in population research to psychiatric problems as well as substance use and related harms, suggesting that individuals with TBI may also present more frequently to mental health and addictions (MHA) services. Little is known, however, about TBI history among MHA service users. The objectives of this review were to understand (i) the prevalence of TBI history among MHA service users; (ii) how TBI history is identified in MHA service settings; and (iii) predictors or outcomes of TBI that have been reported in MHA service users. METHODS: A scoping review was conducted in accordance with PRISMA Scoping Review Extension guidelines. A search for relevant literature was conducted in MEDLINE, PsycINFO, SPORTDiscus, CINAHL, and Embase as well as various gray literature sources. RESULTS: Twenty-eight relevant studies were identified. TBI was defined and operationalized heterogeneously between studies, and TBI history prevalence rates ranged considerably among the study samples. The included studies used varied methods to identify TBI history in MHA settings, such as clinical chart audits, single-item questions, or structured questionnaires (eg, Brain Injury Screening Questionnaire or Ohio State University TBI Identification Method). TBI history was most consistently associated with indicators of more severe substance use problems and mental health symptoms as well as increased aggression or risk to others. Studies reported less consistent findings regarding the relationship of TBI to physical health, cognitive impairment, functioning, risk to self, and type of psychiatric diagnosis. CONCLUSION: Screening for TBI history in MHA settings may contribute important information for risk assessment and care planning. However, to be clinically useful, assessment of TBI history will require consistent operationalization of TBI as well as use of validated screening methods.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".