Community gardens as local learning environments in social housing contexts: participant perceptions of enhanced wellbeing and community connection
Bibliographic record
Abstract
Urban community gardens provide learning environments for diverse groups, including those who may be experiencing health and social inequalities such as residents in social housing communities. Learning to grow fresh food in safe social spaces provides individuals with opportunities to increase awareness of their personal wellbeing and community life. This paper reports on the findings of a research study that explored broader impacts of a community gardening programme on 42 adult residents living in social housing estates in Sydney, Australia. The mixed-methods study design captured participants’ self-perceived benefits of community gardening across six new sites. A final sample of 23 participants across the sites completed both the Sense of Community Index 2 and the Personal Wellbeing Index questionnaires at pre- and post-test (following six to seven months of being involved in the programme). Focus groups involved 42 participants from all six sites. Perceived benefits included enhanced awareness of their overall health and wellbeing, new interest in growing fresh food, enjoyment of shared produce and recipes, feelings of happiness, frequent socialisation and community connectedness. The findings highlight the impactful role of community gardens as effective local learning environments that promote psychological wellbeing and community connection in underserved communities. We conclude by reinforcing the need for sustainable community gardens for addressing social inequality and promoting multiple psychosocial benefits.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".