Integrated Care Pathways for Black Persons With Traumatic Brain Injury: A Critical Transdisciplinary Scoping Review of the Clinical Care Journey
Bibliographic record
Abstract
Objectives: This novel critical transdisciplinary scoping review examined the literature on integrated care pathways that consider Black people living with traumatic brain injury (TBI). The objectives were to (a) summarize the extent, nature, and range of literature on care pathways that consider Black populations, (b) summarize how Blackness, race, and racism are conceptualized in the literature, (c) determine how Black people come to access care pathways, and (d) identify how care pathways in research consider the mechanism of injury and implications for human occupation. Methods: Six databases were searched systematically identifying 178 articles after removing duplicates. In total, 43 articles on integrated care within the context of Black persons with TBI were included. Narrative synthesis was conducted to analyze the data and was presented as descriptive statistics and as a narrative to tell a story. Findings: All studies were based in the United States where 81% reported racial and ethnic disparities across the care continuum primarily using race as a biological construct. Sex, gender, and race are used as demographic variables where statistical data were stratified in only 9% of studies. Black patients are primarily denied access to care, experience lower rates of protocol treatments, poor quality of care, and lack access to rehabilitation. Racial health disparities are disconnected from racism and are displayed as symptoms of a problem that remains unnamed. Conclusion: The findings illustrate how racism becomes institutionalized in research on TBI care pathways, demonstrating the need to incorporate the voices of Black people, transcend disciplinary boundaries, and adopt an anti-racist lens to research.
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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 | 0.024 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".