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Record W2305806706 · doi:10.1177/0306624x16639973

What Does Success Look Like in the Forensic Mental Health System? Perspectives of Service Users and Service Providers

2016· article· en· W2305806706 on OpenAlexaff
James Livingston

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsThematic analysisMental healthService providerContext (archaeology)PsychologyQualitative researchApplied psychologyMental health serviceService (business)Principal (computer security)Social psychologyComputer securityComputer sciencePsychiatrySociologyBusinessMarketing

Abstract

fetched live from OpenAlex

Outcomes research in forensic mental health (FMH) has concentrated on reoffending as the principal indicator of success. Defining success in one-dimensional, negative terms can create a distorted view of the diverse objectives of the FMH system. This qualitative study examined the complexity of success from the perspectives of people in the FMH system. Interviews were conducted with 18 forensic service users and 10 forensic service providers. Data were analyzed inductively using thematic analysis to identify predominant themes. The participants conceptualized success as a dynamic process materializing across six different domains in the context of the FMH system: (a) normal life, (b) independent life, (c) compliant life, (d) healthy life, (e) meaningful life, and (f) progressing life. The results indicate that people who provide or use FMH services emphasize a broad range of processes and outcomes, apart from public safety, when they think about success.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.389
GPT teacher head0.441
Teacher spread0.052 · 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 designQualitative
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

Citations40
Published2016
Admission routes1
Has abstractyes

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