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Record W3196045912 · doi:10.1177/00938548211039877

“Trapped in their Shame”: A Qualitative Investigation of Moral Injury in Forensic Psychiatry Patients

2021· article· en· W3196045912 on OpenAlexaff
Sophia L. Roth, Aamna Qureshi, Heather M. Moulden, Gary Chaimowitz, Ruth A. Lanius, Bruno J. Losier, Margaret C. McKinnon

Bibliographic record

VenueCriminal Justice and Behavior · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsWestern UniversityMcMaster UniversityHomewood Research InstituteSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsShameMoral injuryPsychologyThematic analysisMoralityVulnerability (computing)PopulationFeelingQualitative researchPoison controlSuicide preventionInjury preventionForensic psychiatrySocial psychologyHuman factors and ergonomicsPsychiatryClinical psychologyMedicineMedical emergencyComputer securitySociology

Abstract

fetched live from OpenAlex

Individuals who engage in criminal behavior for which they are found not criminally responsible (NCR) may be at increased vulnerability to experience moral pain and, in extreme circumstances, moral injury after regaining insight into the consequences of their behavior. Yet, almost no research exists characterizing the nature, severity, or impact of moral pain in this population. Semi-structured interviews were conducted with nine forensic psychiatric patients and 21 of their care providers. Narratives were explored using thematic analysis. Findings demonstrate that NCR patients endorse symptoms consistent with moral injury, including feelings of guilt toward victims, shame for one’s behavior, and a loss of trust in one’s morality. Moral pain is a strong driver of behavior and must be understood as part of a constellation of factors influencing criminality, risk, and recovery. Future research must develop adequate tools to measure and characterize offense-related moral injury to understand its impact on this population.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.073
GPT teacher head0.371
Teacher spread0.298 · 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 designObservational
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

Citations19
Published2021
Admission routes1
Has abstractyes

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