Engaging Equity-Deserving Populations in Co-Creation
Bibliographic record
Abstract
Background: Refugee families in Canada experience significant mental health challenges and face heightened barriers to accessing mental healthcare services, yet their voices remain underrepresented in the design of those services. A co-creation approach offers a way to meaningfully engage those equity-deserving populations by centering their experiences and perspectives. However, limited research has specifically examined how to engage refugee families in co-creation processes. This study aimed to review the literature on engaging equity-deserving groups in co-creation and to develop a tailored engagement strategy as part of the Thriving Together co-design project. Methods: A two-phase approach was used. Phase one involved a structured narrative review guided by Arksey and O'Malley's framework. Three databases (PubMed, Web of Science, and Scopus) were searched, along with grey literature and citation searches. A total of 45 studies were included in the review, and findings were charted across definitions, guiding principles, theoretical frameworks, phases of co-creation, engagement methods, barriers, and enablers. Phase two synthesized these findings with field insights from the Thriving Together project to inform a context-specific engagement strategy. Results: The included studies, primarily from high-income countries, focused on equity-deserving populations and revealed inconsistent terminology but recurring emphasis on key engagement principles, such as trust, power-sharing, and flexibility. Common phases of co-creation included preparation, discovery, ideation, and implementation, with frequent use of creative methods like storytelling and visual tools. Reported barriers included power imbalances and logistical challenges. These findings and the practical insights informed the engagement strategy, which is conceptualized as a journey, guided by principles, structured around clear phases, supported by practical tools, and designed to proactively address barriers and promote meaningful participation. Conclusion: More research is needed to advance co-creation with refugee populations. Achieving equitable and impactful engagement relies on operationalizing guiding values such as reflexivity, adaptability, and authentic partnership with the communities involved.
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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 | 0.042 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".