Utilizing trauma-informed approaches in prisons for federally sentenced women: Challenges and recommendations
Bibliographic record
Abstract
This paper examines literature, policy documents, and government reports on trauma-informed care in federal prisons for incarcerated women and the difficulties of providing such care in a carceral environment. Incarcerated women represent a minority of incarcerated people in Canada, and they typically have increased rates of trauma compared to incarcerated males, which impairs their ability to participate in correctional programs fully. Many researchers recommend implementing trauma-informed approaches in correctional settings, and Correctional Service Canada (CSC) has attempted to do so for federally sentenced women by adhering to principles outlined in Creating Choices, a report created to influence the care of incarcerated women. Despite CSC’s efforts to use trauma-informed approaches, the non-therapeutic prison environment and the power dynamics between prisoners and staff make it impossible to provide trauma-informed care in prisons, as women are often re-traumatized in prison due to common security practices such as strip searches. Therefore, this paper offers policy change recommendations, including eliminating strip searches and providing correctional programming led by external treatment providers, that would minimize the harm women experience while incarcerated. However, given the inherent harms associated with prison that cannot be addressed through reform, allowing women to serve their sentences in the community is optimal and necessary to reduce the use of imprisonment and truly provide them with trauma-informed care.
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 | 0.036 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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".