Barriers and facilitators to housing and healthcare services for people experiencing homelessness with concurrent brain injury, mental health and substance use disorders: a qualitative study
Bibliographic record
Abstract
Background Acquired brain injury (ABI) can significantly impact mental health, vulnerability to addictions, and housing stability, yet the intersection of these challenges is understudied. Individuals living with ABI are disproportionately represented among populations experiencing homelessness and have a high prevalence of concurrent mental health and substance use (MHSU) disorders, leading to poorer health outcomes and lower quality of life. The objective of this study was to identify barriers and facilitators to housing and healthcare services for people experiencing homelessness with ABI and concurrent MHSU disorders. Methods Data were collected during a one-day workshop as part of the British Columbia Consensus for Brain Injury, Mental Health and Addiction project. Semi-structured focus groups involving ABI survivors, service providers, and community stakeholders explored barriers, facilitators, and recommendations for service improvements. Using manifest content analysis, data were analyzed in accordance with a well-validated conceptual framework for healthcare access. Results A total of 163 stakeholders (M = 46.40, SD = 13.80, 72% female) including 74 with lived experience of ABI and/or homelessness, participated in the focus groups. Manifest content analysis revealed five barriers and five facilitators: Barriers included (1) Stigma, (2) Insufficient Investment, (3) Siloed Systems, (4) Generalized Approaches to Housing, and (5) Policies that do not Support Complex Needs, while facilitators included (1) Increasing Discourse on the Intersections of ABI, MHSU, and Homelessness, (2) Government Commitment to Systemic Change, (3) Collaboration Across Organizations, (4) Community-Based Services, and (5) Supportive Housing Models. Conclusions These findings highlight gaps in existing policies and services while identifying effective approaches to supporting individuals experiencing these intersections. Efforts to address barriers and leverage existing facilitators may support the development of accessible services that address unmet health and housing needs among people experiencing homelessness with concurrent ABI and MHSU conditions.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".