Outcomes and impacts of community food hubs: a rapid review
Bibliographic record
Abstract
In Australia and other high-income countries, communities are experiencing diet-related diseases due to social inequities and food systems that promote the production and consumption of unhealthy foods(1). Community food hubs have the potential to strengthen local food systems and improve access to healthy, affordable, culturally appropriate food by selling local food to local people(2). The primary aim of this rapid review was to identify short- and medium-term outcomes and long-term impacts associated with community food hubs. In January 2024, four databases and the grey literature were searched for relevant studies and reports published in English between 2013 and 2023. Empirical evaluations of food hubs in high-income countries that included a physical market selling healthy local food were eligible for inclusion. A narrative synthesis was conducted, and descriptive statistics were used to summarise outcomes and impacts under five categories: economic development and viability; ecological sustainability; access to and demand for healthy local food; personal and community wellbeing; and agency and re-localisation of power(3,4). A total of 16 studies/reports were included, reporting on 24 community food hubs (USA n = 16; Australia n = 7; Canada n = 1). Food hubs were often described as farmers’ markets (n = 9, 37% of food hubs), some of which offered financial incentives/subsidies to people living on low incomes. Some food hubs also sold food wholesale and/or provided nutrition education and community gardens. Across the 24 food hubs, a total of 83 short- and medium-term outcomes were assessed. No long-term impacts were evaluated. Outcomes were considered ‘positive’ if evaluation results reflected desirable changes. Overall, 86% of outcomes were positive (n = 71). Within the personal and community wellbeing category, 42 outcomes were assessed, and 83% (n = 35) were positive (e.g., increased fruit and vegetable consumption, increased community connection). Within the access to and demand for healthy local food category, 25 outcomes were assessed, and 96% (n = 24) were positive (e.g., increased access to and/or demand for affordable local produce). Outcomes under the remaining three categories were assessed less frequently. Within the economic development and viability category, 6 outcomes were assessed, and 50% (n = 3) were positive (e.g., access to new markets for food hub suppliers). Within the ecological sustainability category, 6 outcomes were assessed, and 100% (n = 6) were positive (e.g., reduction in food packaging and food waste). Within the agency and re-localisation of power category, 4 outcomes were assessed, and 75% (n = 3) were positive (e.g., integration of community members from low income and cultural minority groups into local food systems). Community food hubs can promote personal and community wellbeing, access to and demand for healthy local food, economic development and viability, ecological sustainability, and agency and re-localisation of power. Future research should focus on methods for evaluating long-term impacts under all five categories.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".