Sexual Recidivism During Treatment: Impact on Therapists
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".