Unleashing compassionate care: canine-assisted intervention as a promising harm reduction approach to prisonization in Canada and its relevance to forensic psychiatry
Bibliographic record
Abstract
In recent years, there has been a global advancement in the offering of canine-assisted interventions (CAI) in prisons. However, these programs have focused primarily on the benefits to the dogs involved and not on the impact on the participants. The authors of this perspective study have been running a CAI program with therapy dogs, called PAWSitive Support, in a Canadian federal prison since 2016. Thoughts from the program facilitators and interviews with prison staff indicate that the program, and specifically the therapy dogs, provides a unique and integrated source of comfort, support, and love for participants. These benefits are consistent with those seen in CAI programs outside of prisons. Unique to the prison setting appears to be an improvement in participant-staff relations. The therapy dogs have helped participants to experience comfort and consequently express their emotions. This seems to contribute to their recognition of support within the prison system and specifically developing trust with staff. Additionally, the dogs have helped to create an experience of the feeling of love within the prison, interpreted as the feeling of being cared for, which is rare for this population. The authors suggest that the integration of a therapy dog intervention in prison could be a novel harm reduction strategy to address issues related to prisonization and associated mental health concerns, including substance use. This consideration can offer unique insight into the field of forensic psychiatry about providing compassionate care to patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".