Mapping the institutionalization of racism in the research about race and traumatic brain injury rehabilitation: implications for Black populations
Bibliographic record
Abstract
PURPOSE: Traumatic brain injury (TBI) is a chronic disease process and a public health concern that disproportionately impacts Black populations. While there is an abundance of literature on race and TBI outcomes, there is a lack of scholarship that addresses racism within rehabilitation care, and it remains untheorized. This article aims to illuminate how racism becomes institutionalized in the scientific scholarship that can potentially inform rehabilitation care for persons with TBI and what the implications are, particularly for Black populations. MATERIAL AND METHODS: Applying Bacchi's What's the Problem Represented to be approach, the writings of critical race theory (CRT) are used to examine the research about race and TBI rehabilitation comparable to CRT in other disciplines, including education and legal scholarship. RESULTS: A CRT examination illustrates that racism is institutionalized in the research about race and TBI rehabilitation through colourblind ideologies, meritocracy, reinforcement of a deficit perspective, and intersections of race and the property functions of whiteness. A conceptual framework for understanding institutional racism in TBI rehabilitation scholarship is presented. CONCLUSIONS: The findings from this article speak to the future of TBI rehabilitation research for Black populations, the potential for an anti-racist agenda, and implications for research and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 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".