A Review of Canine-Assisted Interventions for Youth Involved in the Criminal Justice System
Bibliographic record
Abstract
Given the complex risk factors associated with youth offending, a comprehensive approach is needed to support the reintegration of incarcerated youth. Canine-assisted interventions (CAIs), involving structured, facilitator-guided programs with dogs, have emerged as a strategy to promote human well-being while sometimes attending to animal wellness. This scoping review, conducted following the Joanna Briggs Institute methodology, examines the characteristics and benefits of CAIs for youth aged 12–17 involved in the criminal justice system. This review included studies published in English through 2024, focusing exclusively on dog-based interventions. Studies were sourced from databases and grey literature between 1998 and 2020. This review maps program designs, objectives, participant profiles, practitioner qualifications, dog roles, and reported outcomes for the youth participants and dogs. Findings reveal that some studies reported improved participant behavioural, psychological, and social outcomes, while few found no significant differences or noted symptom worsening. Some studies noted that training dogs in a CAI may boost their chances of adoption. Gaps include non-standardized terminology, need for more research, inadequate handler training, and insufficient focus on animal welfare. The study concludes that standardized protocols, enhanced handler training, and rigorous program evaluation are essential for ethical and effective CAI implementation that contributes to the well-being and successful reintegration of youth following incarceration.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".