Connect, collaborate and tailor: a model of community engagement through infographic design during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background Across the globe, racial and ethnic minorities have been disproportionately affected by COVID-19 with increased risk of infection and burden from disease. Vaccine hesitancy has contributed to variation in vaccine uptake and compromised population-based vaccination programs in many countries. Connect, Collaborate and Tailor (CCT) is a Public Health Agency of Canada funded project to make new connections between public health, healthcare professionals and underserved communities in order to create culturally adapted communication about COVID-19 vaccines. This paper describes the CCT process and outcomes as a community engagement model that identified information gaps and created tailored tools to address misinformation and improve vaccine acceptance. Methods Semi-structured interviews with CCT participants were undertaken to evaluate the effectiveness of CCT in identifying and addressing topics of concern to underserved and ethnic minority communities. Interviews also explored CCT participants’ experiences of collaboration through the development of new partnerships between ethnic minority communities, public health and academic researchers, and the evolution of co-operation sharing ideas and creating infographics. Thematic analysis was used to produce representative themes. The activities described were aligned with the levels of public engagement described in the IAP2 spectrum (International Association for Public Participation). Results Analysis of interviews (n = 14) revealed that shared purpose and urgency in responding to the COVID-19 pandemic motivated co-operation among CCT participants. Acknowledgement of past harm, present health, and impact of social inequities on public service access was an essential first step in establishing trust. Creating safe spaces for open dialogue led to successful, iterative cycles of consultation and feedback between participants; a process that not only helped create tailored infographics but also deepened engagement and collaboration. Over time, the infographic material development was increasingly directed by community representatives’ commentary on their groups’ real-time needs and communication preferences. This feedback noticeably guided the choice, style, and presentation of infographic content while also directing dissemination strategies and vaccine confidence building activities. Conclusions The CCT process to create COVID-19 vaccine communication materials led to evolving co-operation between groups who had not routinely worked together before; strong community engagement was a key driver of change. Ensuring a respectful environment for open dialogue and visibly using feedback to create information products provided a foundation for building relationships. Finally, our data indicate participants sought reinforcement of close cooperative ties and continued investment in shared responsibility for community partnership-based public health.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 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".