Understanding the barriers and facilitators of healthcare services for brain injury and concurrent mental health and substance use issues: a qualitative study
Bibliographic record
Abstract
BACKGROUND: People with acquired brain injury (ABI) may experience concurrent conditions such as, mental health and substance use concerns, that require specialized care. There are services that aim to support people with ABI and these conditions separately; however, little is known about the facilitators and barriers of these services. Therefore, the purpose of this study was to engage stakeholders to investigate the facilitators and barriers of healthcare services for ABI and concurrent issues. METHODS: Semi-structured focus groups were conducted in-person and virtually with people with ABI, caregivers, healthcare professionals, and policy makers during a one-day event in British Columbia, Canada. Manifest content analysis was used with a constructivist perspective to analyze data. RESULTS: 90 participants (including 34 people with ABI) provided insights during 15 simultaneous focus groups. Three categories were identified: (1) complexity of ABI, (2) supports, (3) structure of care. Complexity of ABI outlined the ongoing basic needs after ABI and highlighted the need for public awareness of ABI. Supports outlined healthcare professional and community-based supports. Structure of care described people with ABI needing to meet criteria for support, experiences of navigating through the system and necessity of integrated services. CONCLUSIONS: These findings highlight the facilitators and barriers of healthcare services for ABI and concurrent conditions and provide insights into the changes that may be needed. Doing so can improve the accessibility and quality of ABI healthcare services.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".