A systematic review of horticultural therapy and urban agriculture interventions targeting depression, anxiety, and acute stress disorder
Bibliographic record
Abstract
Mental ill-health is a major concern in urban settings, particularly in relation to conditions such as depression, anxiety and acute stress disorder. Research has demonstrated the potential for horticultural therapy (HT) interventions that draw on urban agriculture practices and methods to address this issue. However, there is a paucity of evidence to support the potential for these interventions for individuals with pre-existing diagnoses. The aim of this systematic review was to address this gap in the literature by evaluating the efficacy of HT interventions as well as the methodologies employed in each study. We searched four bibliographic databases, and identified eleven studies for inclusion in the review, and reported generally favourable results: six studies reported improvements for depressive symptoms; three found that HT interventions mitigated stress; and two studies reported a positive influence on anxiety. Over two thirds of the included studies had either moderate ( n = 6) or high ( n = 2) risk of bias, and there was a high degree of methodological heterogeneity. In addition, the number of studies was small, therefore the generalisability of the findings is limited. Further research is needed to establish a robust causal link between HT interventions and improved mental health outcomes among populations with pre-existing diagnoses. • Mental ill-health remains a persistent concern in urban settings. • Targeted horticultural therapy and urban agriculture interventions can provide a range of mental health benefits. • The present review indicated promising results, despite moderate risk of bias and methodological heterogeneity. • More evidence is needed to establish urban agriculture as a scalable public policy strategy to tackle mental ill-health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".