Barriers, facilitators and solutions to the care of people experiencing homelessness with traumatic brain injury in Quebec, Canada: clinicians’ and concerned parties’ perspectives
Bibliographic record
Abstract
PURPOSE: People experiencing homelessness have disproportionately high rates of traumatic brain injury (TBI), yet services remain inaccessible or poorly adapted to their needs. Limited research has explored the barriers, facilitators and potential solutions to improve healthcare for this population. The objectives were to identify the individual- and environment-level barriers to healthcare for people experiencing homelessness who have sustained a TBI, identify the environment-level facilitators to care for this population, and identify potential solutions to improve care. MATERIAL AND METHODS: = 4). Data were analyzed using Braun and Clarke's thematic analysis approach. RESULTS: Participants reported: (1) healthcare structures misaligned with the realities of people experiencing homelessness; (2) reduced trust in health services by people experiencing homelessness; (3) reliance on overburdened community organizations lacking TBI expertise; and (4) transforming care requires cross-sector collaborations and rethinking current healthcare delivery to provide more flexible TBI services. CONCLUSION: Healthcare for this population is not optimal and fails to meet their needs. Implementing low-threshold service models, fostering collaboration, and providing targeted training could significantly improve TBI care for this 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".