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Record W4383960717 · doi:10.59703/ijrr.v5i2.3-13

Patients’ and family members’ experiences of recovery in a forensic psychiatry program.

2022· article· en· W4383960717 on OpenAlexaff
Ivana Furimsky, Michelle Chen, Fiona Wilson, Gary Chaimowtiz

Bibliographic record

VenueInternational Journal of Risk and Recovery · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsForensic psychiatryForensic sciencePsychiatryPsychologyMedicine

Abstract

fetched live from OpenAlex

The introduction of a recovery approach to forensic psychiatry services has been embraced in recent years.The recovery approach moves patient care beyond the domains of symptom reduction and aggression management.It places the importance on the patient's personal experiences and values, and instills hope for a future with meaningful activities and supportive social relationships.As an initial step to integrating a recovery approach, we sought to better understand patients' and family members' perspectives and experiences of recovery in a forensic psychiatry program (FPP).This project involved one family member and two patient focus groups.All groups were asked what recovery meant to them and what we could do to support their recovery in the FPP.The focus groups were audio recorded and transcribed.A thematic analysis approach identified themes from the transcripts.Family themes included the patient returning to their original identity, opportunities to address the past, developing positive connections with others, balancing rehabilitation in the forensic environment, and maintaining communication with staff.Patient themes included developing positive connections, developing better communication about the forensic system, balancing rehabilitation in the forensic environment, and progressing with their lives.Patients and family members described their experiences of recovery in our FPP.Some areas for improvements were identified, which can form the groundwork for future improvement initiatives in our FPP.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.275

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 head0.048
GPT teacher head0.370
Teacher spread0.322 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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