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Record W2054748733 · doi:10.1080/01612840305320

Therapeutic Relationships and Boundary Maintenance: The Perspective of Forensic Patients Enrolled in a Treatment Program for Violent Offenders

2003· article· en· W2054748733 on OpenAlexaff
Penny Schafer, Cindy Peternelj‐Taylor

Bibliographic record

VenueIssues in Mental Health Nursing · 2003
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of SaskatchewanGenome Prairie
Fundersnot available
KeywordsPerspective (graphical)PsychologyTherapeutic relationshipForensic scienceInterpersonal communicationMental healthInterpersonal relationshipAnalogyClinical psychologyPsychotherapistMedicineSocial psychology

Abstract

fetched live from OpenAlex

To extend current knowledge about therapeutic relationships and boundary maintenance with the incarcerated forensic patient, the focus of this naturalistic inquiry was the exploration of the perspectives of forensic patients enrolled in a treatment program for violent offenders. Twelve male participants were interviewed three times. Eight of the twelve participants were interviewed a fourth time for the purpose of soliciting their feedback regarding the researcher's analysis of the data. Analysis of the data collected revealed a core process--the development of "therapeutic" relationships--indicating that the development of relationships was a complex process. Consistent with the participants' tendency to use terms analogous to a house to describe their experiences, the analogy of a house was used to describe the five interrelated themes that emerged from the data. If the interpersonal relationship is the heart of nursing then forensic mental health nurses need to understand the complexity of therapeutic relationships from the perspective of their patients.

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)0.004
metaresearch head score (Gemma)0.012
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 score0.016
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0160.011
Scholarly communication0.0070.006
Open science0.0020.005
Research integrity0.0040.010
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 head0.049
GPT teacher head0.434
Teacher spread0.385 · 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

Citations51
Published2003
Admission routes1
Has abstractyes

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