Exploring the impact of community gardens on mental health: a scoping review
Bibliographic record
Abstract
Participants engaged in community gardens (CGs) processes experience many positive outcomes, from social networking to intimacy with nature. Yet there exists a gap in the literature on CGs between their co-creative and co-participative practices and the impact of such collaborative social practices on mental health, especially as it relates to structurally marginalized populations. To this end, our scoping review explores what is known about the relationship between CGs and mental health benefits. Arksey and O’Malley’s scoping review method was used and supplemented by Levac, Colquhoun, and O’Brien’s emphasis on research and policy practice implications. Twenty-two studies met the inclusion criteria and they were conducted in the United States, Canada, the United Kingdom, Australia, New Zealand, Spain, Portugal, Japan, and Singapore. Most of the studies used qualitative or mixed methods. The CGs in this review were situated in prisons, university campuses, a church, a shelter, urban rooftops, and urban and rural neighbourhoods. These CGs engaged diverse populations, including immigrants, refugees, newcomers, Indigenous peoples, women, seniors, students, youth, racialized peoples, and persons with disabilities and mental health issues. Our results reveal that the mental health of CG participants is inseparable from engagement processes like collaborative place-making labour that engender social connectedness, collaborative learning, empowerment, and a connection to nature. We gesture to the affinities between this co-creative and co-participative process and similar land and/or place-based practices with an eye to the potential for civic participation and/or awareness of human rights to advance mental health equity.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".