Characterizing traumatic brain injury and its association with homelessness in a community-based sample of precariously housed adults and youth
Bibliographic record
Abstract
ABSTRACT We characterized the prevalence, mechanisms, and sex difference of lifetime traumatic brain injury (TBI) in a precariously housed sample. We also examined the impact of TBI severity and timing on becoming and staying homeless. 285 precariously housed participants (adults n = 226, youths n = 59) completed the Brain Injury Screening Questionnaire (BISQ) in addition to other health assessments. A history of TBI was reported in 82.1% of the sample, with 64.6% reporting > 1 TBI, and 21.4% reporting a moderate or severe TBI (msTBI). 10.1% of adults had traumatically-induced lesions on MRI scans. Assault was the most common mechanism of injury overall, and females reported significantly more TBIs due to physical abuse than males (adjusted OR = 1.26, 95% CI = 1.14 – 1.39, p = 9.18e -6 ). The first msTBI was significantly closer to the first experience of homelessness ( b = 2.79, p = 0.003) and precarious housing ( b = 2.69, p = 7.47e -4 ) than was the first mild TBI. Traumatic brain injuries more proximal to the initial loss of stable housing were associated with a longer lifetime duration of homelessness (RR = 1.04, 95% CI = 1.02 – 1.06, p = 6.8e -6 ) and precarious housing (RR = 1.03, 95% CI = 1.01 – 1.04, p = 5.5e -10 ). These findings demonstrate the high prevalence of TBI in vulnerable persons and the severity- and timing-related risk that TBI may confer for the onset and prolongation of homelessness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".