MétaCan
Menu
Back to cohort
Record W3047113249 · doi:10.1080/1068316x.2020.1798429

Institutional adjustment of young adults undergoing forensic assessment

2020· article· en· W3047113249 on OpenAlexaff
N. Zoe Hilton, Carla Cesaroni, Elke Ham, Tracey A. Skilling

Bibliographic record

VenuePsychology Crime and Law · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsOntario Tech UniversityCentre for Addiction and Mental HealthWaypoint Centre for Mental Health CareUniversity of Toronto
Fundersnot available
KeywordsPsychosocialYoung adultPsychiatryPsychologyVulnerability (computing)PsychosisMedicineClinical psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Adolescence and emerging adulthood are considered distinct developmental periods and times of peak criminal offending and onset of many major mental disorders. Previous research suggests that adolescents and young adults in adult correctional institutions adjust more poorly than their older counterparts do, and psychosocial adjustment is associated with pre-existing vulnerability. Research with institutionalized young adults is sparse. We studied institutional and psychosocial adjustment of men admitted to a forensic psychiatric hospital. Overall, younger age on admission was associated with previous vulnerability (i.e. adverse childhood experiences) and institutional adjustment (e.g. assaults, management problems), but not psychosocial adjustment (i.e. mood problems, psychosis, social withdrawal). Age had small and inconsistent effects on adjustment measures in regression analyses controlling for length of stay. Comparing the 141 young adults aged 18–24 with 141 men aged 25–59 matched on pre-admission psychiatric and criminal history did not yield age-related group differences. The apparent poorer adjustment of young adults may be attributable to younger onset of psychiatric and criminal justice involvement, resulting in earlier admission to the institution.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.819
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
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.344
Teacher spread0.305 · 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 designTheoretical or conceptual
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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuePsychology Crime and LawSame topicPsychopathy, Forensic Psychiatry, Sexual OffendingFrench-language works237,207