Employing diffusion of innovation theory for ‘not missing the mass’ in community-engaged research
Bibliographic record
Abstract
INTRODUCTION: Engaging with minority communities, such as immigrants and ethnic minorities, often involves adopting top-down approaches, wherein researchers and policymakers provide solutions based on their perspective. However, these approaches may not adequately address the needs and preferences of the community members, who have valuable insights and experiences to share. Therefore, community-engaged approaches, which involve collaborative partnerships between community members and researchers to identify issues, co-create solutions, and recommend policy changes, are becoming more recognized for their effectiveness and relevance. Yet, prevailing community engagement efforts often focus on easily reachable and already engaged segments of the community, sometimes overlooking the broader population. METHODS: When working with immigrant and racialized communities, we encountered difficulties in engaging the wider community through traditional researcher-led approaches. We realized that overcoming these challenges required innovative strategies rooted in community-based participatory research principles and the diffusion of innovation theory. We recognized that a nuanced understanding of the community's dynamics and preferences was crucial in shaping our approach and building trust and rapport with the community members. RESULTS: The need to bridge the gap between researcher-led initiatives and community-driven involvement has never been more pronounced. Our experience, chronicled in this article, highlights the journey of our research program with an immigrant/racialized community. This reflection enhances our comprehension of community engagement that deliberately strives to reach a larger cross-section of the community. By providing practical methods for reaching the broader community and navigating the intricacies of engagement, we aim to assist fellow researchers in conducting effective community-engaged research across various minority communities. CONCLUSION: In sharing our insights and successful strategies for community engagement, we hope to contribute to the field's knowledge. Our commitment to fostering meaningful collaboration underscores the importance of co-creating solutions that resonate with the diverse voices within these communities. Through these efforts, we envision a more inclusive and impactful approach to addressing the complex challenges faced by minority populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.174 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.068 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".