Equity considerations in clinical practice guidelines for traumatic brain injury and the criminal justice system: A systematic review
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) is disproportionately prevalent among individuals who intersect or are involved with the criminal justice system (CJS). In the absence of appropriate care, TBI-related impairments, intersecting social determinants of health, and the lack of TBI awareness in CJS settings can lead to lengthened sentences, serious disciplinary charges, and recidivism. However, evidence suggests that most clinical practice guidelines (CPGs) overlook equity and consequently, the needs of disadvantaged groups. As such, this review addressed the research question "To what extent are (1) intersections with the CJS considered in CPGs for TBI, (2) TBI considered in CPGs for CJS, and (3) equity considered in CPGs for CJS?". METHODS AND FINDINGS: CPGs were identified from electronic databases (MEDLINE, Embase, CINAHL, PsycINFO), targeted websites, Google Search, and reference lists of identified CPGs on November 2021 and March 2023 (CPGs for TBI) and May 2022 and March 2023 (CPGs for CJS). Only CPGs for TBI or CPGs for CJS were included. We calculated the proportion of CPGs that included TBI- or CJS-specific content, conducted a qualitative content analysis to understand how evidence regarding TBI and the CJS was integrated in the CPGs, and utilised equity assessment tools to understand if and how equity was considered. Fifty-seven CPGs for TBI and 6 CPGs for CJS were included in this review. Fourteen CPGs for TBI included information relevant to the CJS, but only 1 made a concrete recommendation to consider legal implications during vocational evaluation in the forensic context. Two CPGs for CJS acknowledged the prevalence of TBI among individuals in prison and one specifically recommended considering TBI during health assessments. Both CPGs for TBI and CPGs for CJS provided evidence specific to a single facet of the CJS, predominantly in policing and corrections. The use of equity best practices and the involvement of disadvantaged groups in the development process were lacking among CPGs for CJS. We acknowledge limitations of the review, including that our searches were conducted in English language and thus, we may have missed other non-English language CPGs in this review. We further recognise that we are unable to comment on evidence that is not integrated in the CPGs, as we did not systematically search for research on individuals with TBI who intersect with the CJS, outside of CPGs. CONCLUSIONS: Findings from this review provide the foundation to consider CJS involvement in CPGs for TBI and to advance equity in CPGs for CJS. Conducting research, including investigating the process of screening for TBI with individuals who intersect with all facets of the CJS, and utilizing equity assessment tools in guideline development are critical steps to enhance equity in healthcare for this disadvantaged group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.419 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".