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Record W2759327064 · doi:10.1192/pb.bp.116.055509

Measuring relational security in forensic mental health services

2017· review· en· W2759327064 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBJPsych Bulletin · 2017
Typereview
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsLearning Partnership
Fundersnot available
KeywordsComputer scienceMental healthConsistency (knowledge bases)Quality (philosophy)Internal consistencyScale (ratio)Relational databaseRelational modelProcess (computing)Computer securityData scienceData miningPsychologyPsychiatryPsychometricsClinical psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Aims and method Relational security is an important component of care and risk assessment in mental health services, but the utility of available measures remains under-researched. This study analysed the psychometric properties of two relational security tools, the See Think Act (STA) scale and the Relational Security Explorer (RSE). Results The STA scale had good internal consistency and could highlight differences between occupational groups, whereas the RSE did not perform well as a psychometric measure. Clinical implications The measures provide unique and complimentary perspectives on the quality of relational security within secure services, but have some limitations. Use of the RSE should be restricted to its intended purpose; to guide team discussions about relational security, and services should refrain from collecting and aggregating this data. Until further research validates their use, relational security measurement should be multidimensional and form part of a wider process of service quality assessment.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.582
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.005

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 head0.147
GPT teacher head0.400
Teacher spread0.253 · 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