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Record W4317436446 · doi:10.1177/10790632231153636

Sexual Recidivism During Treatment: Impact on Therapists

2023· article· en· W4317436446 on OpenAlexaffabout
Michel Raymond, Jean Proulx, Geneviève Ruest, Sébastien Brouillette‐Alarie

Bibliographic record

VenueSexual Abuse · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité de MontréalInstitut national de psychiatrie légale Philippe-Pinel
Fundersnot available
KeywordsRecidivismSadnessPsychologyIntervention (counseling)CognitionClinical psychologyPsychiatryAnger

Abstract

fetched live from OpenAlex

There are few studies of therapists' reactions to working with individuals who have committed sexual offenses, and almost none on reactions following sexual recidivism by a patient who is currently in treatment. Consequently, the aim of the current study was to analyze the cognitive and emotional reactions, as well as the intervention strategies, of therapists who have learned of the sexual recidivism of a patient. A total of 59 participants from the province of Quebec (Canada) completed a questionnaire on their reactions to this event. Participants' responses to their patient's recidivism varied as a function of gender, experience, and the way they learned of the recidivism. The most common cognitions reported were thinking of the victim and thinking about the consequences of further judicialization for the patient and those close to them. The most common emotions reported were sadness for the victim and fear that the patient would reoffend again. The most common intervention strategies were being sensitive to the experience of the patient and asking the patient what drove them to offend. Support measures for therapists working with individuals who have committed sexual offenses during treatment are discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.012

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 head0.045
GPT teacher head0.354
Teacher spread0.310 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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