An optimal trauma-informed pathway for PTSD, complex PTSD and other mental health and psychosocial impacts of trauma in prisons: an expert consensus statement
Bibliographic record
Abstract
People in prisons have high levels of trauma exposure throughout their lives. Presentations are often complex, with a high prevalence of PTSD and CPTSD and other mental health comorbidities. Prisons themselves can be stressful and traumatising environments. There are challenges in the delivery of effective treatments for PTSD and CPTSD. There is a need for the development of effective clinical pathways for these conditions that are embedded within trauma-informed organisational approaches. Responding to this need, this report is the result of a multidisciplinary expert consensus meeting and review of the research literature on PTSD, CPTSD, associated comorbidities and optimal approaches to trauma-informed practice. The group consisted of 24 expert representatives from psychology, psychiatry, healthcare, academia, social care and Welsh Government. The meeting commenced with presentations on various aspects of the clinical pathway for PTSD and complex PTSD in prisons, and of applications of trauma-informed practice within prisons. Small sub-groups then provided practical recommendations and solutions relevant to their assigned topic. Findings were presented to all meeting attendees for another round of discussion and debate, until consensus was reached. The resulting recommendations provide guidance to improve identification, treatment and support for people living in prison who have experienced trauma.
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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 | 0.165 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".