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Record W2066053650 · doi:10.1080/14999013.2013.763390

Perceptions of Treatment Planning in a Forensic Mental Health Hospital: A Qualitative, Participatory Action Research Study

2013· article· en· W2066053650 on OpenAlexaffabout
James Livingston, Alicia Nijdam‐Jones, Tharun Krishnan R

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2013
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsSimon Fraser UniversityBC Mental Health & Substance Use ServicesUniversity of British Columbia
Fundersnot available
KeywordsQualitative researchHonestyThematic analysisOpenness to experienceMental healthParticipatory action researchNursingCitizen journalismPsychologyAction (physics)NarrativeMedicineMedical educationPsychiatrySociologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

This qualitative, participatory action research study examined treatment planning from the perspectives of 29 inpatients and 16 service providers at a Canadian forensic mental health hospital. Qualitative data produced by indepth interviews were analyzed using thematic analysis. Participants’ narratives clustered around six themes: (a) It's all about the patient: Involving patients; (b) Other professionals at the table: Including other professionals; (c) Onward and upward: Progressing through the hospital; (d) Know me for who I am: Understanding the patient; (e) Keep me in the loop: Sharing information with patients; and (f) To trust or not to trust: Openness, honesty, and trust.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.039
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0250.018
Scholarly communication0.0070.005
Open science0.0030.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.303
GPT teacher head0.601
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 source (direct Gemma or distilled Codex), 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

Citations31
Published2013
Admission routes2
Has abstractyes

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