An Experiential Approach to Canine-Assisted Learning in Corrections for Prisoners Who Use Substances
Bibliographic record
Abstract
ABSTRACT: Canine-assisted interventions are a promising approach to help address substance use and mental health issues in prisons. However, canine-assisted interventions in prisons have not been well explored in relation to experiential learning (EL) theory, despite canine-assisted interventions and EL aligning in many ways. In this article, we discuss a canine-assisted learning and wellness program guided by EL for prisoners with substance use issues in Western Canada. Letters written by participants to the dogs at the conclusion of the program suggest that such programming can help shift relational dynamics and the prison learning environment, benefit prisoners' thinking patterns and perspectives, and help prisoners generalize and apply key learnings to recovery from addiction and mental health challenges. Implications are discussed in relation to clinicians' practices, prisoners' health and wellness, and prison programming.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".