Understanding Trauma-Informed Care in Correctional Facilities: A Scoping Review
Bibliographic record
Abstract
People who are incarcerated are significantly more likely to have experienced traumatic events than others in the general population. Trauma-informed care (TIC) is an approach that recognizes and responds to the lasting effects of trauma on peoples' lives and health, going beyond individually focused, trauma-specific care and into broader change in policy and practice. Our objectives were to describe how TIC is implemented in correctional facilities, and evidence on the impacts of TIC in correctional facilities. We conducted a scoping review of academic and gray literature. Two team members screened titles and abstracts and reviewed full texts for eligibility. We included articles in English focused on TIC in any adult correctional facility and extracted relevant data. We categorized information on how TIC is implemented into structural, organizational, and individual levels, and organized evidence on the impacts of TIC into the Quintuple Aim for Health Care Improvement framework. We identified 45 relevant articles, including 14 studies that reported evidence on impacts of TIC across the Quintuple Aim components. While the correctional facility environment challenges TIC implementation, TIC interventions at the individual, structural, and organizational levels could improve health outcomes in correctional facilities.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".