Critical Characteristics of Housing and Housing Supports for Individuals with Concurrent Traumatic Brain Injury and Mental Health and/or Substance Use Challenges: A Qualitative Study
Bibliographic record
Abstract
Traumatic brain injury (TBI) and mental health and/or substance use challenges (MHSU) are commonly co-occurring and prevalent in individuals experiencing homelessness; however, evidence suggests that systems of care are siloed and organized around clinical diagnoses. Research is needed to understand how housing and housing supports are provided to this complex and understudied group in the context of siloed service systems. This study aimed to describe critical characteristics of housing and housing supports for individuals with concurrent TBI and MHSU from the perspectives of service users with TBI and MHSU and housing service providers. Using basic qualitative description, in-depth interviews were conducted with 16 service users and 15 service providers. Data were analyzed using thematic analysis techniques. Themes capture core processes in finding and maintaining housing and the critical housing supports that enabled them: (1) overcoming structural barriers through service coordination, education and awareness raising, and partnerships and collaborations; and (2) enabling engagement in meaningful activity and social connection through creating opportunities, training and skills development, and design of home and neighborhood environments. Implications for practice, including the urgent need for formalized TBI and MHSU education, support for service providers, and potential interventions to further enable core housing processes are discussed.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".