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Record W4402166454 · doi:10.1080/1068316x.2024.2394807

An optimal trauma-informed pathway for PTSD, complex PTSD and other mental health and psychosocial impacts of trauma in prisons: an expert consensus statement

2024· article· en· W4402166454 on OpenAlexaff
Clare Crole-Rees, Jack Tomlin, Natasha Kalebic, Sarah Argent, Claudia Berrington, Jason Davies, Matthew Hoskins, Lucie James, Manuela Jarrett, O.St John, Lewis Jones, Radha Kothari, Imogen Kretzschmar, Michael S. Martin, Iain McKinnon, Gwen O'Connor, Madeline Petrillo, M. Phillips, Rob Poole, Isidora Popovic, Alexander I. F. Simpson, Pamela J. Taylor, Sarah Wigham, Andrew Forrester

Bibliographic record

VenuePsychology Crime and Law · 2024
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsCentre for Addiction and Mental HealthUniversity of TorontoMinistry of Community Safety and Correctional Services
FundersCardiff University
KeywordsPsychosocialMental healthStatement (logic)PsychologyPsychiatryClinical psychologyPsychotherapistPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.440
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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