“If you do not have Black futures in mind…then what’s guiding the steps”: anti-racist recommendations for traumatic brain injury rehabilitations’ investments in hopeful Black futures
Bibliographic record
Abstract
PURPOSE: The need for specialized rehabilitation considerations to address racial health disparities and optimize functional outcomes such as participation in daily life for Black people with traumatic brain injury (TBI) has been acknowledged. This study uses anti-racism as an entry point for addressing institutional racism by examining what the experiences of Black survivors of TBI, rehabilitation providers, and family caregivers tell us about imagined possibilities for rehabilitation to promote Black futures. MATERIAL AND METHODS: A constructivist-informed narrative inquiry using critical race theory and in-depth narrative interviewing was applied across ten women and four men. Reflexive thematic analysis within and across groups of participants led to conceptualizing two main themes and five sub-themes. RESULTS: Conceptualized themes captured requirements for TBI rehabilitations' investments in Black futures: (1) the need for critical information and specialized educational supports and particular requirements for supporting participation in meaningful life situations, and (2) responsibilities of delivering rehabilitation care for Black service users. CONCLUSION: TBI rehabilitation must be tailored to the realities of living while being Black, integrate personal values, beliefs, interests, and equitable supports to maximize optimal functioning and participation if the goal of rehabilitation is community integration for all persons living with the impacts of TBI.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".