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Record W2124130689 · doi:10.1177/1078390313489730

Supporting Recovery by Improving Patient Engagement in a Forensic Mental Health Hospital

2013· article· en· W2124130689 on OpenAlexaffabout
James Livingston, Alicia Nijdam‐Jones, Sara Lapsley, Colleen Calderwood, Johann Brink

Bibliographic record

VenueJournal of the American Psychiatric Nurses Association · 2013
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsSimon Fraser UniversityBC Mental Health & Substance Use ServicesUniversity of British Columbia
Fundersnot available
KeywordsMental healthPeer supportEmpowermentNursingIntervention (counseling)Qualitative researchPsychologyStigma (botany)Mental illnessMedicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Mental health services are shifting toward approaches that promote patients' choices and acknowledge the value of their lived experiences. OBJECTIVE: To support patients' recovery and improve their experiences of care in a Canadian forensic mental health hospital, an intervention was launched to increase patient engagement by establishing a peer support program, strengthening a patient advisory committee, and creating a patient-led research team. DESIGN: The effect of the intervention on patient- and system-level outcomes was studied using a naturalistic, prospective, longitudinal approach. Quantitative and qualitative data were gathered from inpatients and service providers twice during the 19-month intervention. RESULTS: Despite succeeding in supporting patients' participation, the intervention had minimal impacts on internalized stigma, personal recovery, personal empowerment, service engagement, therapeutic milieu, and the recovery orientation of services. Peer support demonstrated positive effects on internalized stigma and personal recovery. CONCLUSIONS: Strengthening patient engagement contributes toward improving experiences of care in a forensic hospital, but it may have limited effects on outcomes.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.367
Teacher spread0.346 · 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

Citations64
Published2013
Admission routes2
Has abstractyes

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