Brain injury, mental health and substance use in homeless populations: community-generated recommendations for healthcare service delivery and research
Bibliographic record
Abstract
BACKGROUND: The prevalence of acquired brain injury (ABI) and mental health/substance use (MHSU) disorders is high amongst people experiencing homelessness, yet guidance for addressing these complex comorbidities is lacking. Therefore, the objective of this study was to engage community-based stakeholders in a health priority-setting process to generate, identify and prioritize recommendations for clinical practice and research to improve healthcare services for individuals with concurrent ABI-MHSU who are experiencing homelessness. METHODS: = 46.40, SD = ± 13.80, 72% female), including service providers, people with lived experience, healthcare professionals and other community-based stakeholders. Stakeholders participated in concurrent focus groups based on the nominal group technique. Initial recommendations were generated then collated, themed and rank-ordered by priority and a consensus voting method was used to identify the top five priorities for research and clinical practice. RESULTS: Stakeholders discussions and subsequent prioritization evaluations identified the following recommendations for clinical practice: (1) Provide accessible and affordable supportive housing; (2) enhance resources (financial, human) for healthcare service providers; (3) design needs-based services that promote quality of life; (4a) improve communication and collaboration between service providers; (4b) adopt a long-term and integrated approach; and (5) reduce stigma and discrimination through public health education. Recommendations for research, also ordered by priority, included: (1) Evaluate and optimize existing interventions for immediate implementation; (2) develop specialized interventions and diagnostic techniques; (3) collect meaningful data to better understand impacts and intersections; (4) increase mechanisms for knowledge transfer; and (5) explore methods for risk identification and prevention. CONCLUSIONS: This is the first study to identify and prioritize recommendations for research and clinical practice related to healthcare services for people experiencing homelessness with concurrent ABI-MHSU conditions. The stakeholder-generated recommendations from this study provide a valuable resource for researchers, clinicians and policymakers to enhance care for this underserved population.
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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 | 0.125 | 0.170 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".