Barriers and Facilitators to Engagement in Collective Gardening Among Black African Immigrants in Alberta, Canada
Bibliographic record
Abstract
Background: Community gardens are increasingly popular in Canadian cities, serving as transformative spaces where immigrants can develop self-reliant strategies for accessing culturally familiar and healthy nutritious foods. However, numerous facilitators and barriers exist that limit the engagement of racialized groups such as Black-identifying immigrants. Using a socio-ecological framework, this research explores barriers and facilitators of engagement of Black African immigrants in collective community gardening in Alberta, Canada. Methods: The study adopted a community-based participatory research (CBPR) approach using mixed methods. Data collection included structured surveys (n = 119) to assess general engagement, facilitators, and barriers; in-depth interviews (n = 10) to explore lived experiences; and Afrocentric sharing circles (n = 2) to probe collective perspectives in relation to engagement in collective gardening. Participants were purposefully recruited through community networks within African immigrant-serving community organizations. Results: Our findings revealed how barriers at various levels of the socio-ecological model (SEM) interact to shape the interest and engagement of African immigrants in collective community gardening. Access to collective gardens was associated with significant benefits, including maintaining healthy foodways, knowledge exchange, growing social capital, and community connections that support overall wellbeing. Conclusions: This study contributes an accessible framework for understanding and addressing the complex barriers that limit engagement in community gardens for vulnerable communities, while highlighting opportunities for creating more inclusive and culturally responsive urban agriculture initiatives.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".