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Record W4212793960 · doi:10.1080/24732850.2021.2016115

Shame among Forensic and Non-Forensic Patients and the Impact of the Social Determinants of Health: A Pilot Study

2022· article· en· W4212793960 on OpenAlexaff
Rusan Lateef, Fiona Moloney, Amina Ali, Roland M. Jones

Bibliographic record

VenueJournal of Forensic Psychology Research and Practice · 2022
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsCentre for Addiction and Mental HealthUniversity of TorontoMcGill University
Fundersnot available
KeywordsForensic scienceShamePsychologyClinical psychologyCriminologyApplied psychologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Shame is associated with various mental health and social difficulties. A broad range of social factors increase the risk of shame among individuals. One group that experiences increased shame due to their mental health diagnoses and criminal justice involvement is forensic patients. The purpose of this pilot study was to compare shame levels in forensic and non-forensic patients with a diagnosis of schizophrenia or other psychotic disorder, and to examine whether any social determinants of health impacted the levels of shame among both groups. A self-report shame questionnaire and a measure of experiences with various social determinants of health were completed by 43 patients with schizophrenia or other psychotic disorder (22 forensic and 21 non-forensic patients). Statistical analyses revealed no significant differences in levels of shame between the forensic and non-forensic patients. Early life experience as well as employment and working conditions had the most significant impact on shame levels for both forensic and non-forensic patients. We also found a significant difference between forensic and non-forensic scores in employment and working conditions (p = .046), with forensic patients rating their experiences with this factor as more positive than non-forensic participants. Clinical implications are discussed.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.191
GPT teacher head0.522
Teacher spread0.331 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2022
Admission routes1
Has abstractyes

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