Implementing Interventions for Women and Youth with Traumatic Brain Injury at Transition from Custodial Settings: A Call to Action
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a serious public health concern and overrepresented among justice-involved populations. An emerging area of research focuses on the complex, interrelated and unmet health and social needs of justice-involved women and youth with TBI. Evidence of these needs continues to grow, yet the health and justice systems continue to underperform in supporting the health and social care of justice-involved women and youth. This commentary is a call to action to begin to redress these gaps. We first provide an overview of the needs of women and youth with TBI that affect their transition from custody to community, including those related to victimization, trauma, mental health, substance use, and homelessness. We then highlight the current gaps in knowledge and practice with respect to interventions for women and youth with TBI at transition from custody. The available evidence for the impact of interventions on people with head injury who are justice-involved is sparse, especially studies of interventions focused on women and youth. We conclude with a call for implementation science studies to support translation from research to practice, emphasizing that researchers, practitioners, policy makers, and women and youth at transition should collaborate to develop, implement, and evaluate accommodations and interventions for TBI. To have meaningful, positive impacts on the systems that serve these women and youth, interdisciplinary service delivery approaches should aim to prevent, raise awareness, identify, and provide timely support and services for the varied needs of women and youth with TBI in transition.
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 | 0.040 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.018 | 0.030 |
| Insufficient payload (model declined to judge) | 0.010 | 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".