Exploring the Meaning of Therapeutic Horticulture for Anishinabek Youth in a Brief Residential Treatment Unit: A Community Engagement CSL Case Study
Bibliographic record
Abstract
This paper explored community-university engagement that integrated a short-term treatment facility for Indigenous youth, a social enterprise organization that focused on healing through horticulture therapy experiences and an interdisciplinary academic team. The focus was to discover whether a horticulture therapy (HT) approach held promise in terms of an appropriate way to expand community service-learning (CSL) with Indigenous peoples and to encourage more diversity of voices in community service-learning experiences. Youth participants took part in a photovoice study and further semi-structured interviews to document their perspectives on the meaning of their horticultural experiences. Findings revealed that youth valued the overall HT experience itself; being connected to the gardens and nature and the social interactions exploring spirituality and the self were significant and meaningful for them. Further, findings demonstrated that a collaborative partnership that engaged multiple service agencies to explore novel ways for engaging youth in healing activities with a university team that guided the research approach holds promise as a CSL with Indigenous youth. We conclude with recommendations on the significance of community-university engagement in delivering therapeutic horticulture programs for Indigenous youth as a community service-learning initiative.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".