Community gardens as psychosocial interventions for refugees and migrants: a narrative review
Bibliographic record
Abstract
Purpose Community gardens are increasingly used as interventions during the resettlement of refugees and other migrants. Little is known about how garden programs might support their mental health and wellbeing. Given the links between climate change and forced migration, community gardens are especially relevant, as they can also support climate change mitigation. This study aims to document psychosocial outcomes of gardening programs for refugees and migrants, and mechanisms leading to these outcomes. Design/methodology/approach The authors searched major databases and the grey literature up to 2021, resulting in the inclusion of 17 peer-reviewed and 4 grey literature articles in a thematic, qualitative analysis. Findings Four consistent themes arose from the analysis: community gardening programs promoted continuity and adaptation (81% of articles), social connectedness (81%), overall wellbeing (95%) and a sense of meaning and self-worth (67%). The results suggest that community gardens can strengthen psychosocial pillars that are key to the recovery and resettlement of refugees and migrants. The land-based and social nature of community gardening may enable connections to the land and others, nurture a sense of belonging in the host country and provide a link to the past for those from agricultural backgrounds. Research limitations/implications Further participatory action research is needed to develop guidelines for the successful implementation of community gardens by resettlement organisations. Originality/value This review indicates that community gardens can be effective psychosocial interventions as part of a network of services supporting the resettlement of refugees and migrants.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".