Hands-on-ground in a new country: a community-based participatory evaluation with immigrant communities in Southern Alberta
Bibliographic record
Abstract
Immigrants experience a high risk of mental health deterioration following settlement in Canada. Immigrant communities benefit from health-promoting interventions that stimulate social inclusion and belonging as protective factors. In this context, community gardens have been recognized as interventions that promote healthy behaviours, place attachment and belonging.This article summarizes our experience conducting a community-based participatory evaluation (CBPE), engaging community stakeholders in planning, implementing and evaluating a community garden for immigrants. We conducted a CBPE to provide relevant and timely feedback to inform programme adaptation and development. Participants, interpreters and organizers were engaged through surveys, focus groups and semi-structured interviews. Participants expressed a range of motivations, benefits, challenges and recommendations. The garden was a place that fostered learning and promoted healthy behaviours, including physical activity and socialization. However, there were challenges in organization and communication with participants. Findings were used to adapt the activities to immigrants' needs and expand the programming of collaborating organizations. Stakeholder engagement facilitated capacity building and direct use of findings. This approach may catalyse sustainable community action with immigrant communities.
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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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".