Co-designing interventions to increase food access: perceptions and experiences of community member end-users
Bibliographic record
Abstract
Abstract Background Access to healthy, affordable food is a challenge in many communities. There is growing recognition that co-designed, community-led approaches, which directly involve end-users in the development, implementation, and evaluation processes, are needed to create effective and contextually appropriate food access interventions. However, limited research has examined the experiences and perceptions of the end-users who play a central role in these processes. This study aims to explore the perceptions and experiences of community member end-users (termed Community Advisors) involved in a co-designed food access initiative titled food uniting neighbours (f.u.n.). Methods A qualitative approach was used. Data were collected through focus groups and individual interviews with f.u.n. Community Advisors (n = 12). Results Four major themes were identified: 1) Motivation to be a Community Advisor, including social connection, helping others, and skill-building; 2) Importance of Community Advisors to the Co-Designed Project, highlighting their role in humanizing the project, building trust, and ensuring community relevance; 3) Facilitators of Community Advisor Success such as mutual respect, teamwork, and administrative support; and 4) Suggestions for Improvement which emphasized the need for greater cultural diversity among the Advisors and more sustainable funding structures. Conclusions Community member end-users play a vital role in co-designed food access solutions contributing authenticity, trust, and deep community insight. Findings suggest that inclusive representation, supportive team dynamics, and stable funding may help sustain meaningful engagement of community members in co-designed initiatives. These findings underscore the importance of developing co-designed processes that are not only empowering but also structurally supported to ensure long-term impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.589 | 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 teacher head, 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".